{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Climate Change Prediction Using ARIMA, SARIMA and RNNs\n",
    "\n",
    "### In this project we try to forecast future climate change based on historical patterns. We collected that data from https://datahub.io/core/global-temp/datapackage.json. You can download the data from:\n",
    "### 1. Link provided\n",
    "### 2. From my github https://github.com/neelabhpant/Deep-Learning-in-Python/blob/master/ClimateChange_MonthlyStart.csv or\n",
    "### 3. Install datapackage library\n",
    "### 4. Dataset contains global land and ocean temperature monthly anomalies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##\n",
    "##\n",
    "##\n",
    "### We begin by importing useful libraries for this project. \n",
    "#### Pandas for reading the data\n",
    "#### Numpy for faster matrix operations\n",
    "#### Sklearn for preprocessing and evaluation\n",
    "#### datapackage to load the data using a url\n",
    "#### Statstools to build statistical models\n",
    "#### Keras (Tensorflow background) to build RNNs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import datapackage\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Getting data straight from the website\n",
    "## You need to have datapackage library installed \n",
    "## Just pip install datapackage\n",
    "\n",
    "data_url = 'https://datahub.io/core/global-temp/datapackage.json'\n",
    "\n",
    "package = datapackage.Package(data_url)\n",
    "\n",
    "resources = package.resources\n",
    "for resources in resources:\n",
    "    if resources.tabular:\n",
    "        data = pd.read_csv(resources.descriptor['path'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Date</th>\n",
       "      <th>Mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GCAG</td>\n",
       "      <td>2016-12</td>\n",
       "      <td>0.7895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GISTEMP</td>\n",
       "      <td>2016-12</td>\n",
       "      <td>0.8100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GCAG</td>\n",
       "      <td>2016-11</td>\n",
       "      <td>0.7504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GISTEMP</td>\n",
       "      <td>2016-11</td>\n",
       "      <td>0.9300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GCAG</td>\n",
       "      <td>2016-10</td>\n",
       "      <td>0.7292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Source     Date    Mean\n",
       "0     GCAG  2016-12  0.7895\n",
       "1  GISTEMP  2016-12  0.8100\n",
       "2     GCAG  2016-11  0.7504\n",
       "3  GISTEMP  2016-11  0.9300\n",
       "4     GCAG  2016-10  0.7292"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('ClimateChange_MonthlyStart.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "## Converting date column to datetime type\n",
    "## Setting Date as index\n",
    "\n",
    "data['Date'] = pd.to_datetime(data['Date'])\n",
    "data.set_index('Date',inplace=True,drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-12-01</th>\n",
       "      <td>GCAG</td>\n",
       "      <td>0.7895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-01</th>\n",
       "      <td>GISTEMP</td>\n",
       "      <td>0.8100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-01</th>\n",
       "      <td>GCAG</td>\n",
       "      <td>0.7504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-01</th>\n",
       "      <td>GISTEMP</td>\n",
       "      <td>0.9300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-01</th>\n",
       "      <td>GCAG</td>\n",
       "      <td>0.7292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Source    Mean\n",
       "Date                       \n",
       "2016-12-01     GCAG  0.7895\n",
       "2016-12-01  GISTEMP  0.8100\n",
       "2016-11-01     GCAG  0.7504\n",
       "2016-11-01  GISTEMP  0.9300\n",
       "2016-10-01     GCAG  0.7292"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10a25eb38>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot GCAG and GISTEMP\n",
    "\n",
    "plt.figure(figsize=(20,6))\n",
    "plt.plot(data[data['Source']=='GCAG']['Mean'],label='GCAG')\n",
    "plt.plot(data[data['Source']=='GISTEMP']['Mean'],label='GISTEMP')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Since, the dataframe comes with observations from 2 sources, namely GCAG and GISTEMP, we will consider only one source (i.e.) GCAG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "GCAG = data[data['Source']=='GCAG']\n",
    "GCAG = GCAG.sort_index()\n",
    "GCAG.index.freq = 'MS'\n",
    "GCAG = GCAG.drop(columns=['Source'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a208cb208>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "GCAG.plot(figsize=(20,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## In order to begin analyzing the data, we begin by testing whether or not the data is stationary. We use Dicky Fuller test to do so. You can read more about Dicky Fuller here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1911068"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dicky Fuller Test\n",
    "### To test stationarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-Fuller Test: DFT\n",
      "ADF test statistic        -0.499547\n",
      "p-value                    0.892105\n",
      "# lags used               24.000000\n",
      "# observations          1619.000000\n",
      "critical value (1%)       -3.434396\n",
      "critical value (5%)       -2.863327\n",
      "critical value (10%)      -2.567721\n",
      "Weak evidence against the null hypothesis\n",
      "Fail to reject the null hypothesis\n",
      "Data has a unit root and is non-stationary\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "def adf_test(series,title=''):\n",
    "    \"\"\"\n",
    "    Pass in a time series and an optional title, returns an ADF report\n",
    "    \"\"\"\n",
    "    print(f'Augmented Dickey-Fuller Test: {title}')\n",
    "    result = adfuller(series.dropna(),autolag='AIC') # .dropna() handles differenced data\n",
    "    \n",
    "    labels = ['ADF test statistic','p-value','# lags used','# observations']\n",
    "    out = pd.Series(result[0:4],index=labels)\n",
    "\n",
    "    for key,val in result[4].items():\n",
    "        out[f'critical value ({key})']=val\n",
    "        \n",
    "    print(out.to_string())          # .to_string() removes the line \"dtype: float64\"\n",
    "    \n",
    "    if result[1] <= 0.05:\n",
    "        print(\"Strong evidence against the null hypothesis\")\n",
    "        print(\"Reject the null hypothesis\")\n",
    "        print(\"Data has no unit root and is stationary\")\n",
    "    else:\n",
    "        print(\"Weak evidence against the null hypothesis\")\n",
    "        print(\"Fail to reject the null hypothesis\")\n",
    "        print(\"Data has a unit root and is non-stationary\")\n",
    "        \n",
    "\n",
    "adf_test(GCAG['Mean'], title='DFT')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This step lets you deconstruct the time series into several components like:\n",
    "### 1. Trend: Reflects long term progression of series\n",
    "### 2. Seasonality: Reflects seasonal variation and checks if seasonal pattern exists in the time series\n",
    "### 3. Residual: Shows irregular component (or \"noise\") at time t, which describes random, irregular influences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## ETS Graph (Error Trend Seasonality)\n",
    "\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "\n",
    "result = seasonal_decompose(GCAG['Mean'],model='add')\n",
    "result.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b990278>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## There exists 12 months seasonality in the dataset\n",
    "\n",
    "result.seasonal.loc['2010-01-01':'2012-01-01'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ARIMA: AR, I, MA\n",
    "## AR or Autoregression (p): A regression model that uses the dependent relationship between the current observation and observations over a previous period\n",
    "## I or Integrated (d): Differencing of observations (substracting an observation from an observation at the previous time step) to make the time series stationary\n",
    "## MA or Moving Average (q): Using past errors to predict the current value. To put it simply if the series is stationary then it will have a constant mean, however at any point in time it may not actually be at the mean. The distance from the mean is called the error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ACF: Auto-Correlation Function gives us values of auto-correlation of any series with its lagged values.\n",
    "### PACF: Partial Auto-Correlation Function finds correlation of the residuals with the next lag value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## ACF and PACF\n",
    "\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "\n",
    "GCAG.plot();\n",
    "plot_acf(GCAG,lags=5);\n",
    "plot_pacf(GCAG,lags=5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## To check if there exists aggregated monthly and quaterly seasonality over years\n",
    "\n",
    "from statsmodels.graphics.tsaplots import month_plot, quarter_plot\n",
    "\n",
    "month_plot(GCAG['Mean']);\n",
    "dfq = GCAG['Mean'].resample(rule='Q').mean()\n",
    "quarter_plot(dfq);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total years of data: 137\n"
     ]
    }
   ],
   "source": [
    "years = len(np.unique(GCAG.index.year))\n",
    "print('Total years of data: %d' %years)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Divide the dataset into training and validation sets\n",
    "### Setting training set from: January 1980 to December 2015\n",
    "### Setting validation set from: January 2016 to December 2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Taking last one year of data as testing data\n",
    "\n",
    "train = GCAG.iloc[:-12]\n",
    "test = GCAG.iloc[-12:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train size: 1632\n",
      "Test size: 12\n"
     ]
    }
   ],
   "source": [
    "print(\"Train size: %d\"%train.shape[0])\n",
    "print(\"Test size: %d\"%test.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The auto_arima function fits the best ARIMA model to a univariate time series according to a provided information criterion (either AIC, AICc, BIC or HQIC). The function performs a search (either stepwise or parallelized) over possible model & seasonal orders within the constraints provided, and selects the parameters that minimize the given metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/pmdarima/arima/_auto_solvers.py:207: ModelFitWarning: Unable to fit ARIMA for order=(2, 1, 3); data is likely non-stationary. (if you do not want to see these warnings, run with error_action=\"ignore\")\n",
      "  ModelFitWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/pmdarima/arima/_auto_solvers.py:207: ModelFitWarning: Unable to fit ARIMA for order=(2, 1, 2); data is likely non-stationary. (if you do not want to see these warnings, run with error_action=\"ignore\")\n",
      "  ModelFitWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARIMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>D.y</td>       <th>  No. Observations:  </th>   <td>1643</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>          <td>ARIMA(1, 1, 3)</td>  <th>  Log Likelihood     </th> <td>1560.759</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>0.094</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 22 May 2019</td> <th>  AIC                </th> <td>-3109.519</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>10:07:06</td>     <th>  BIC                </th> <td>-3077.093</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                <td>1</td>        <th>  HQIC               </th> <td>-3097.494</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                       <td> </td>        <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>     <td>    0.0006</td> <td>    0.000</td> <td>    2.058</td> <td> 0.040</td> <td> 2.69e-05</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.D.y</th> <td>    0.8897</td> <td>    0.026</td> <td>   34.372</td> <td> 0.000</td> <td>    0.839</td> <td>    0.940</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.D.y</th> <td>   -1.3690</td> <td>    0.036</td> <td>  -37.750</td> <td> 0.000</td> <td>   -1.440</td> <td>   -1.298</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2.D.y</th> <td>    0.3358</td> <td>    0.041</td> <td>    8.124</td> <td> 0.000</td> <td>    0.255</td> <td>    0.417</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L3.D.y</th> <td>    0.0460</td> <td>    0.028</td> <td>    1.639</td> <td> 0.101</td> <td>   -0.009</td> <td>    0.101</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th> <td>           1.1239</td> <td>          +0.0000j</td> <td>           1.1239</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>           1.0234</td> <td>          +0.0000j</td> <td>           1.0234</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.2</th> <td>           2.0482</td> <td>          +0.0000j</td> <td>           2.0482</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.3</th> <td>         -10.3722</td> <td>          +0.0000j</td> <td>          10.3722</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             ARIMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                    D.y   No. Observations:                 1643\n",
       "Model:                 ARIMA(1, 1, 3)   Log Likelihood                1560.759\n",
       "Method:                       css-mle   S.D. of innovations              0.094\n",
       "Date:                Wed, 22 May 2019   AIC                          -3109.519\n",
       "Time:                        10:07:06   BIC                          -3077.093\n",
       "Sample:                             1   HQIC                         -3097.494\n",
       "                                                                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.0006      0.000      2.058      0.040    2.69e-05       0.001\n",
       "ar.L1.D.y      0.8897      0.026     34.372      0.000       0.839       0.940\n",
       "ma.L1.D.y     -1.3690      0.036    -37.750      0.000      -1.440      -1.298\n",
       "ma.L2.D.y      0.3358      0.041      8.124      0.000       0.255       0.417\n",
       "ma.L3.D.y      0.0460      0.028      1.639      0.101      -0.009       0.101\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1            1.1239           +0.0000j            1.1239            0.0000\n",
       "MA.1            1.0234           +0.0000j            1.0234            0.0000\n",
       "MA.2            2.0482           +0.0000j            2.0482            0.0000\n",
       "MA.3          -10.3722           +0.0000j           10.3722            0.5000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pmdarima import auto_arima\n",
    "\n",
    "stepwise_fit = auto_arima(GCAG['Mean'],start_p=0,start_q=0,max_p=5,max_q=3,seasonal=False)\n",
    "stepwise_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARIMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>D.Mean</td>      <th>  No. Observations:  </th>   <td>1631</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>          <td>ARIMA(1, 1, 3)</td>  <th>  Log Likelihood     </th> <td>1550.423</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>0.093</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 22 May 2019</td> <th>  AIC                </th> <td>-3088.845</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>10:07:12</td>     <th>  BIC                </th> <td>-3056.464</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>           <td>02-01-1880</td>    <th>  HQIC               </th> <td>-3076.832</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                 <td>- 12-01-2015</td>   <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>        <td>    0.0006</td> <td>    0.000</td> <td>    2.043</td> <td> 0.041</td> <td> 2.34e-05</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.D.Mean</th> <td>    0.8912</td> <td>    0.026</td> <td>   33.868</td> <td> 0.000</td> <td>    0.840</td> <td>    0.943</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.D.Mean</th> <td>   -1.3750</td> <td>    0.037</td> <td>  -37.494</td> <td> 0.000</td> <td>   -1.447</td> <td>   -1.303</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2.D.Mean</th> <td>    0.3408</td> <td>    0.042</td> <td>    8.189</td> <td> 0.000</td> <td>    0.259</td> <td>    0.422</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L3.D.Mean</th> <td>    0.0471</td> <td>    0.028</td> <td>    1.665</td> <td> 0.096</td> <td>   -0.008</td> <td>    0.102</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th> <td>           1.1221</td> <td>          +0.0000j</td> <td>           1.1221</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>           1.0238</td> <td>          +0.0000j</td> <td>           1.0238</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.2</th> <td>           2.0182</td> <td>          +0.0000j</td> <td>           2.0182</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.3</th> <td>         -10.2846</td> <td>          +0.0000j</td> <td>          10.2846</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             ARIMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                 D.Mean   No. Observations:                 1631\n",
       "Model:                 ARIMA(1, 1, 3)   Log Likelihood                1550.423\n",
       "Method:                       css-mle   S.D. of innovations              0.093\n",
       "Date:                Wed, 22 May 2019   AIC                          -3088.845\n",
       "Time:                        10:07:12   BIC                          -3056.464\n",
       "Sample:                    02-01-1880   HQIC                         -3076.832\n",
       "                         - 12-01-2015                                         \n",
       "================================================================================\n",
       "                   coef    std err          z      P>|z|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "const            0.0006      0.000      2.043      0.041    2.34e-05       0.001\n",
       "ar.L1.D.Mean     0.8912      0.026     33.868      0.000       0.840       0.943\n",
       "ma.L1.D.Mean    -1.3750      0.037    -37.494      0.000      -1.447      -1.303\n",
       "ma.L2.D.Mean     0.3408      0.042      8.189      0.000       0.259       0.422\n",
       "ma.L3.D.Mean     0.0471      0.028      1.665      0.096      -0.008       0.102\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1            1.1221           +0.0000j            1.1221            0.0000\n",
       "MA.1            1.0238           +0.0000j            1.0238            0.0000\n",
       "MA.2            2.0182           +0.0000j            2.0182            0.0000\n",
       "MA.3          -10.2846           +0.0000j           10.2846            0.5000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA,ARIMAResults\n",
    "model = ARIMA(train['Mean'],order=(1, 1, 3))\n",
    "results = model.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = len(train)\n",
    "end = len(train) + len(test) - 1\n",
    "\n",
    "predictions = results.predict(start=start,\n",
    "                              end=end,\n",
    "                              typ='levels').rename('ARIMA (1,1,3) Predictions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c279e5b00>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIsAAAGCCAYAAABguEMBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzs3Xd4VGX6xvH7zKRM2iSkQgihCdJraAG7CCoiTQQRRUSauG6xrLs/XXVXV9eyFgRsiAUjoggqq6yuotK70pFeAymQSuqc3x8TQkIKQZKclO/nus41M6fNM2PMBTfv+7yGaZoCAAAAAAAAJMlmdQEAAAAAAACoOQiLAAAAAAAAUIiwCAAAAAAAAIUIiwAAAAAAAFCIsAgAAAAAAACFCIsAAAAAAABQiLAIAAAAAAAAhQiLAAAAAAAAUIiwCAAAAAAAAIU8rHrj0NBQs1mzZla9PQAAAAAAQJ2zfv36RNM0wy7mHpaFRc2aNdO6deusensAAAAAAIA6xzCMAxd7D6ahAQAAAAAAoBBhEQAAAAAAAAoRFgEAAAAAAKCQZT2LAAAAAACojXJzc3X48GFlZWVZXQrqMYfDoaioKHl6elb6vQmLAAAAAAC4AIcPH1ZAQICaNWsmwzCsLgf1kGmaSkpK0uHDh9W8efNKvz/T0AAAAAAAuABZWVkKCQkhKIJlDMNQSEhIlY1uIywCAAAAAOACERTBalX5M0hYBAAAAAAAgEKERQAAAAAA1DKGYej2228vfJ2Xl6ewsDANGjTIwqpQVxAWAQAAAABQy/j5+WnLli06ffq0JOmbb75R48aNLa4KdQVhEQAAAAAAtdANN9ygxYsXS5Li4uI0evTowmMZGRkaP368evbsqa5du2rRokWSpP379+uyyy5Tt27d1K1bN61YsUKStHTpUl155ZUaMWKE2rRpozFjxsg0zer/UKgRPKwuAAAAAACA2uqJL7Zq29HUSr1nu0in/nZT+/OeN2rUKD355JMaNGiQfvnlF40fP14//fSTJOmpp57S1VdfrdmzZ+vUqVPq2bOnrr32WoWHh+ubb76Rw+HQr7/+qtGjR2vdunWSpI0bN2rr1q2KjIxU3759tXz5cvXr169SPxtqB8IioAbJys3Xjvg0bT2aoq1HU7X1aKoaOr317PBOCvL1sro8AAAAADVIp06dtH//fsXFxemGG24oduy///2vPv/8cz3//POSpKysLB08eFCRkZGaNm2aNm3aJLvdrl27dhVe07NnT0VFRUmSunTpov379xMW1VOERYBFUjJzC0OhbcdStfVoivYkZCjf5R7q6XR4qE0jp77fkaARs1bq3fE91TjIx+KqAQAAABRVkRFAVWnw4MF64IEHtHTpUiUlJRXuN01Tn376qS699NJi5z/++OOKiIjQzz//LJfLJYfDUXjM29u78LndbldeXl7VfwDUSIRFQBUzTVPxqVnaeiS1YLSQOyA6cup04TkNnQ61j3RqQPuGah/pVPvIQEU18JFhGFq5J0kT31+nYTOWa85dPdW2kdPCTwMAAACgJhk/fryCgoLUsWNHLV26tHD/gAED9Oqrr+rVV1+VYRjauHGjunbtqpSUFEVFRclms+ndd99Vfn6+dcWjxiIsAiqRy2VqX1JGYSi0rWAqWXJGjiTJMKTmIX7qGh2k23s3VftIp9pFOhXq713mPfu0DNH8yX00bvZajZy1Uq/f0V2xLUOr6yMBAAAAqMGioqL0u9/9rsT+Rx99VL///e/VqVMnuVwuNW/eXF9++aWmTp2q4cOH67333tPAgQPl5+dnQdWo6QyrupvHxMSYZ5poAbVRdl6+dsWnu0OhY+5QaPuxVGXmuJN5T7uh1hEBhSOF2kc61aaRU/7evy2jPXrqtO6cvUYHkjL1wsjOuqlzZGV+HAAAAAAVtH37drVt29bqMoBSfxYNw1hvmmbMxdyXkUVABaRl5RaOEjozamj3iXTlFfQX8vOyq12kUyNjmqhdpFPtI51qFR4gLw9bpdUQGeSj+ZP76J731um+uI06npqlCZe1qLT7AwAAAAAgERYBJZxIy3I3nS7SX+hAUmbh8VB/b7WPdOrqNuGFI4aig31lsxlVXluQr5fev7uXfv/RJv1j8XbFp2TpLze0rZb3BgAAAADUD4RFqLdcLlMHkzMLViM7u1R9Qlp24TnRwb5qH+nULd2jCoOhcKejnLtWPYenXa+N6aYnvtiqt5bt0/G0bD1/Syd5e9gtrQsAAAAAUDcQFqFeyM136dfj6WeXqi/oL5SW7V4K0m4z1CrcX5e1Ci0MhdpFOuV0eFpceensNkNPDG6vRoE+evbrHUpMy9brd3SvsfUCAAAAAGoPwiLUORnZedoRX9Bb6Eiqth5L0a74dOXkuyRJPp52tW0UoCFdGxf2F2odESCHZ+0amWMYhqZc2VIRTm899MkvGjlrpd4d31MRFo98AgAAAADUboRFqNWSM3IKRwudaTy9LzFDZxb5a+DrqfaRgbqrb7OCYChQzUP9ZK9DPX6GdYtSqL+3pnywXsNmrNC743vokvAAq8sCAAAAANRSlbdUE1CFTNPUoeRMfb0lXi/+d6funrNWvZ/+n7r9/RuNfXuNnvlqhzYcOKmWYf66/5pWevOOGK3489Xa8Gh/fTChlx65oa1u7tJYl4T716mg6IzLW4dp3qQ+ys5zafjMlVq3P9nqkgAAAABUsYULF8owDO3YsaNw3/79++Xj46MuXbqoXbt2uuOOO5SbmytJWrp0qQYNGiRJmjNnjgzD0Lffflvifp988knhvsTERHl6emrWrFnl1jJixAjt3btXkvTXv/5VTZo0kb+/f5nn79ixQ3369JG3t7eef/75Ms8bOHCgOnfurPbt22vy5MnKz8+XJD3wwAP67rvvSr1m3Lhxat68ubp06aJu3bpp5cqV5dZ+Ps2aNVNiYqIkKTY2ttxz58yZo6NHjxa+njBhgrZt23ZR72+F844sMgxjtqRBkk6YptmhlONjJD0syZCUJmmKaZo/V3ahqD/y8l3am5jhHjF0JLWgAXWqUk67f8HZDKlFmL96tQhW+4LRQu0aOdXAz8viyq3VoXGgFkyJ1Z3vrNGYt1brldFdNaB9Q6vLAgAAAFBF4uLi1K9fP8XFxemJJ54o3N+yZUtt2rRJ+fn56t+/vz7++GONGTOmxPUdO3bURx99pGuvvbbwfp07dy52zvz589W7d2/FxcVp8uTJpdaxdetW5efnq0WLFpKkm266SdOmTVOrVq3KrD04OFivvPKKFi5cWO5n/Pjjj+V0OmWapkaMGKH58+dr1KhRuu+++3TPPffo6quvLvW65557TiNGjNB///tfTZo0Sb/88kux43l5efLwuPDJVitWrCj3+Jw5c9ShQwdFRkZKkt56660Lfo+aoCLfzBxJ0yW9V8bxfZKuME3zpGEY10t6Q1KvyikPdV1Wbr62H3OHQWemku04lqrsPHd/IS8Pm9o2DNANHRsVBENOtWnolI9X7eovVF2iQ3z16ZRYjZ+zVlM+WK8nbu6gsb2bWl0WAAAAgEqWnp6uZcuW6fvvv9dNN91ULCw6w263q2fPnjpy5Eip97jsssv0008/KTc3V9nZ2dq9e7e6dOlS7Jy4uDi98MILuu2223T48GFFRUWVuM/cuXN18803F77u3bv3eesPDw9XeHi4Fi9eXO55TqdTkjvcycnJkWG4Z4o0bdpUSUlJio+PV8OGZf8j+eWXX67du3dLkq688kp16dJFy5Yt0+jRo3XHHXdo8uTJOnjwoCTppZdeUt++fZWUlKTRo0fryJEj6tOnj8wzfU4k+fv7Kz09XZL07LPP6oMPPpDNZtP111+vmJgYrVu3TmPGjJGPj49Wrlyp66+/Xs8//7xiYmIUFxenp59+WqZp6sYbb9Szzz5beM/7779fX375pXx8fLRo0SJFRERo/vz5euKJJ2S32xUYGKgff/zxvN9rZTlvWGSa5o+GYTQr53jRWG2VpJI/OYCklMzcIv2F3I97EtLlKvj/LsDhofaRTt3eu2nhiKGWYX7ysDNb8kIE+3kp7p7emvbhBj26cIviU07rgesuLfylCgAAAKASffVnKX5z5d6zYUfp+mfKPWXRokUaOHCgWrdurZCQEK1fv17du3cvdk5WVpZWr16tl19+udR7GIaha6+9VkuWLFFKSooGDx6sffv2FR4/dOiQjh07pp49e2rkyJGaN2+e/vSnP5W4z/LlyzV69Ojf8EErZsCAAVqzZo2uv/56jRgxonB/t27dtHz5cg0fPrzMa7/44gt17Nix8HVOTo7WrVsnSbrtttv0hz/8Qf369dPBgwc1YMAAbd++XU888YT69eunxx57TIsXL9bbb79d4r5fffWVFi1apNWrV8vX11fJyckKDg7W9OnTC8Ohoo4ePaqHH35Y69evV4MGDXTddddp4cKFGjJkiDIyMtS7d2899dRTeuihh/Tmm2/q//7v//Tkk09qyZIlaty4sU6dOnWxX+MFqewG13dL+qqS74laxjRNxadmFU4hOxMMHTl1uvCcCKe32kcGamCHhoXBUFQDHwKNSuLjZdfrY7vr0UVb9Nr3exSfkq1nhneUJ8EbAAAAUCfExcXp/vvvlySNGjVKcXFxhWHRnj171KVLF+3bt0833nijOnXqVOZ9Ro0apVdeeUUpKSl64YUX9PTTTxcemzdvnkaOHFl43vjx40sNi44dO6awsLDK/HjFLFmyRFlZWRozZoy+++479e/fX5J7dFLR/kBFPfjgg/rHP/6hsLCwYmHPrbfeWvj822+/LdZPKDU1Venp6frxxx+1YMECSdKNN96oBg0alLj/t99+q7vuuku+vr6S3NPqyrN27VpdeeWVhd/TmDFj9OOPP2rIkCHy8vIq7CXVvXt3ffPNN5Kkvn37aty4cRo5cqSGDRtW/pdUySotLDIM4yq5w6J+5ZwzUdJESYqOjq6st4aFXC5T+5IyCkOhbQVTyZIzciRJhiE1D/FT1+gg3d67aeFS9aH+3hZXXvd52G16emhHNXT66N/f7lJCerZmjukmP28WQQQAAAAqzXlGAFWF5ORkfffdd9q8ebMMw1B+fr4Mw9Bzzz0n6WzPosTERPXt21eff/65Bg8eXOq9evbsqc2bN8vX11etW7cudiwuLk7x8fGaO3euJPfomF9//bVELyIfHx9lZWVVwSc9y+Fw6Oabb9aiRYsKw6KsrCz5+PiUev6ZnkXn8vPzK3zucrm0atUqORyOqim6gjw9PQsHTtjtduXl5UmSZs2apdWrV2vx4sXq3r271q9fr5CQkGqpqVKGGRiG0UnSW5JuNk0zqazzTNN8wzTNGNM0Y6oydUTVyM7L1+bDKfpozUE9unCLhs1Yrg6PL9E1L/yg38Vt1Oxl+5SckaNr24bricHt9cnkPtr8+AB998CVmn5bN025sqWuaB1GUFSNDMPQ/de20jPDOmr57kSNemOVEtKyrS4LAAAAwEX45JNPNHbsWB04cED79+/XoUOH1Lx5c/3000/FzgsNDdUzzzyjf/7zn+Xe75lnnik2okiSdu3apfT0dB05ckT79+/X/v379cgjjyguLq7E9W3bti3sC1Sezz77TI888sh5z7vmmmt05MgRpaen69ixY5LcPYsWL16sNm3aFKuxQ4cS63BV2HXXXadXX3218PWmTZskufscffjhh5Lc081OnjxZ4tr+/fvrnXfeUWZmpiR3gCdJAQEBSktLK3F+z5499cMPPygxMVH5+fmKi4vTFVdcUW59e/bsUa9evfTkk08qLCxMhw4d+m0f9De46CEGhmFES1ogaaxpmrsuviTUBGlZuYWjhM6MGtp9Il15BQ2G/Lzsahfp1MiYJoWjhVqFB8jLg2lONdGontEKC/DWvR9u0PCZK/Tu+J5qHup3/gsBAAAA1DhxcXF6+OGHi+0bPnx4qfuHDBmixx9/vESQVNT1119f6nsMHTq0xHvceuuteuyxx4rtv/HGG7V06dLCVdUeeughffjhh8rMzFRUVJQmTJigxx9/XHv27ClsWB0fH6+YmBilpqbKZrPppZde0rZt2+Tv76/du3crODhYqampGjx4sLKzs+VyuXTVVVcVrsiWm5ur3bt3l+gNdCFeeeUV3XvvverUqZPy8vJ0+eWXa9asWfrb3/6m0aNHq3379oqNjS11ZtTAgQO1adMmxcTEyMvLSzfccIOefvppjRs3TpMnTy5scH1Go0aN9Mwzz+iqq64qbHBdtCl4aR588EH9+uuvMk1T11xzTYmV6qqSUbSrd6knGEacpCslhUo6LulvkjwlyTTNWYZhvCVpuKQDBZfkmaZ53v9aMTEx5pmmUrBeTp5LH6w6oHUHkrX1aKoOJGUWHgv191K7yMDC1cjaRwaqabCvbDb6C9U2Gw+e1N3vuv+/mz2uh7o0CbK4IgAAAKD22b59u9q2bWt1GTXG6dOnddVVV2n58uWy28teufr222/Xv//973L7G23ZskWzZ8/Wiy++WO57fvbZZ9qwYYP+/ve//+a664LSfhYNw1hfkVymPOcNi6oKYVHNkZCWralz12vt/pOKDvYtFgq1j3Qq3Gnt/E1Urr0J6brznTVKTMvRa2O66uo2EVaXBAAAANQqhEUlLVmyRG3btq22/sTz589X//79FRRUv/8BvKrCIjrd1nMbD57UlA826NTpHL0yuqsGd460uiRUsRZh/lowpa/umrNG97y3Xk8P7aBbe9BwHgAAAMBvN2DAgGp9v1tuuaVa36++ocFMPTZv7UHd+voqeXoYWjClL0FRPRIW4K2PJvZRbMsQPfzpZr38rXseLAAAAICK4c/PsFpV/gwSFtVDOXku/fWzzXr4083q1SJYn9/bT+0inVaXhWrm7+2ht+/soWFdG+vf3+7SXz7borx8l9VlAQAAADWew+FQUlISgREsY5qmkpKS5HBUTdsYpqHVMydSszRl7gatP3BSk65ooQevu1QedjLD+srLw6YXRnZWw0CHZizdo4S0bL06uqt8vMpuSgcAAADUd1FRUTp8+LASEhKsLgX1mMPhUFRUVJXcm7CoHll/4KSmfLBeaVl5mn5bVw3qxLQzSIZh6KGBbRThdOjxL7ZqzFur9PadPdTAz8vq0gAAAIAaydPTU82bN7e6DKDKMKSknvhw9UGNemOlHJ52fXZvLEERSrgztplmjummLUdTNXzWCh1KzrS6JAAAAACABQiL6rjsvHw9suAX/eWzzerTMlSfT+urNg3pT4TSDezQSHMn9FJiWraGzVyhLUdSrC4JAAAAAFDNCIvqsOOpWRr1xirFrTmkqVe21DvjeijIl6lFKF+PZsH6dEqsPG2GRr2xSst+TbS6JAAAAABANSIsqqPW7U/WoFeXaWd8mmaM6aaHBraR3WZYXRZqiVYRAVowta+iGvho3DtrtHDjEatLAgAAAABUE8KiOsY0Tb2/6oBGvbFKfl52Lby3r27o2MjqslALNQx06OPJfRTTrIF+P2+TZv2wh6VBAQAAAKAeICyqQ7Jy8/XnTzfr0YVbdFmrUC2a1k+tIwKsLgu1mNPhqXfH99SgTo30zFc79MQX25TvIjACAAAAgLrMw+oCUDmOpZzW5A826OdDp3Tf1ZfoD9e2lo1pZ6gE3h52vTKqqyKcDr29bJ9OpGXpxZFd5PC0W10aAAAAAKAKEBbVAWv2JWvq3PU6nZOvWbd318AODa0uCXWMzWbo0UHt1CjQoX8s3q7E9DV6c2yMAn09rS4NAAAAAFDJmIZWi5mmqfdW7tdtb66S0+Gphff2JShClZpwWQu9MrqrNh48qVteX6Gjp05bXRIAAAAAoJIRFtVSWbn5evCTX/TYoq26onWYFk7rq1b0J0I1GNw5Uu/e1VPHTmVp2IwV2hmfZnVJAAAAAIBKRFhUCx09dVojX1+pT9Yf1v3XtNKbd8TI6WA6EKpP7CWhmjepj1ymqRGzVmjV3iSrSwIAAAAAVBLColpm5Z4k3fTqMu1NyNCbd8ToD/1pZA1rtIt0asHUWEU4Hbrj7TVa/Msxq0sCAAAAAFQCwqJawjRNzV62T7e/vVpBvu7+RP3bRVhdFuq5qAa++mRyH3WMCtS0uA16Z/k+q0sCAAAAAFwkwqJaICs3X3/6+Gc9+eU2Xd0mXAvv7atLwv2tLguQJAX5emnuhF7q3zZCT3yxTf/8artcLtPqsgAAAAAAv5GH1QWgfIdPZmrS++u17Viq/ti/taZddQnTzlDjODztmnl7d/3t8y16/Ye9Op6SpX+N6CwvD/JoAAAAAKhtCItqsBV7EjXtw43KzXPprTtidE1bpp2h5rLbDP395g5qFOij55bsVGJ6jmbe3k0BNF8HAAAAgFqFf/avgUzT1Fs/7dXYt9co2M9Li6b1JShCrWAYhu696hI9N6KTVu5N0q2vr9KJ1CyrywIAAAAAXADCohrmdE6+fj9vk/6xeLuubevuT9QijP5EqF1uiWmit++M0f6kDA2buUJ7EtKtLgkAAAAAUEGERTXIoeRMDZ+5Qp//fFQPDrhUM8d0l783MwVRO115abg+mthbWbn5Gj5zhdYfOGl1SQAAAACACiAsqiGW/Zqom6Yv06GTmZp9Zw/dSyNr1AGdooL06ZRYBfl46rY3V+mbbcetLgkAAAAAcB6ERRYzTVNv/LhHd8xerfAAb30xrZ+uahNudVlApWka4qdPpsSqTcMATXp/neauPmB1SQAAAACAchAWWSgzJ0/3xW3U0//ZoYEdGuqzqX3VLNTP6rKAShfq7624ib11Resw/fWzLXrxvztlmqbVZQEAAAAASkFDHIscTMrUxPfXaefxND08sI0mX9FChsG0M9Rdvl4eevOOGP3ls8165bvdik/N0lNDO8rTTmYNAAAAADUJYZEFftiVoN/FbZQkzbmrp65oHWZxRUD18LDb9OzwTmoY6KNX/verEtKy9dqYbvL14lcRAAAAANQU/JN+NTJNUzOX7tFd76xRo0CHPp/Wl6AI9Y5hGPpj/9Z6emhH/bArQaPfWKXE9GyrywIAAAAAFCAsqiYZ2Xma9uFGPfv1Dl3fsZEWTI1V0xD6E6H+uq1XtF4fG6Odx9M0YuYKHUjKsLokAAAAAIAIi6rF/sQMDZuxQl9tOaZHrm+j6aO7Mu0GkNS/XYTmTuitlNO5GjZjhX45fMrqkgAAAACg3iMsqmLf7zyhwdOX6Xhalt4d31OTrmhJI2ugiO5NG+iTKbHy8bJr1Bur9P3OE1aXBAAAAAD1GmFRFTFNU699v1vj56xV4wa++mJaP13Wiv5EQGlahvlrwZRYNQvx04R312n+ukNWlwQAAAAA9RZhURVIz87TlA826LklO3VTp0gtmBKrJsG+VpcF1GjhTofmTeqtPi1C9OAnv+i173fLNE2rywIAAACAeoewqJLtS8zQ0NeW67/b4vV/N7bVy6O6yMfLbnVZQK0Q4PDU7HE9NKRLpJ5bslOPLdqqfBeBEQAAAABUJ7osV6LvdhzX/R9tkofN0Ad391LsJaFWlwTUOl4eNr04sosiAh16/Ye9Op6apVdGd5XDk9AVAAAAAKoDI4sqgctl6pX//aq7312n6GBffXFfP4Ii4CLYbIYeub6t/nZTO32z/bjGvLVapzJzrC4LAAAAAOoFwqKLlJaVq0kfrNeL3+zSkC6N9emUWEU1oD8RUBnu6ttc00d30+bDKRo+c4UOn8y0uiQAAAAAqPMIiy7CnoR0DXltub7bcUKPDWqnF0d2ZqoMUMlu7NRI793dUyfSsjVsxgptO5pqdUkAAAAAUKcRFv1G32w7riHTl+tkZq4+uLuXxvdrLsMwrC4LqJN6twjRJ5NjZbcZGvn6Sq3YnWh1SQAAAABQZxEWXSCXy9RL3+7SPe+tU7NQP31xXz/1aRlidVlAnXdpwwB9OiVWkUEO3fnOGi3adMTqkgAAAACgTiIsugCpWbma+P46vfTtrxrWrbHmT+6jxkE+VpcF1BuRQT6aPzlWXaMb6P6PNunNH/daXRIAAAAA1DmERRW0+0SahkxfrqU7E/TE4PZ64Rb6EwFWCPTx1Hvje+rGjo301H+26+9fbpPLZVpdFgAAAADUGR5WF1AbLNkarz/O2yQfL7vmTuilXi2YdgZYyeFp16ujuyoswFtvL9un+NQsvTiys7w9CHABAAAA4GIRFpXD5TL172936dXvdqtzVKBmje2uRoFMOwNqApvN0N9uaqdGgQ7986sdSkrP1utjYxTo42l1aQAAAABQqzENrQwpp3N197tr9ep3u3VL9yjNm9SHoAioYQzD0KQrWuqlW7to/YGTuvX1lYpPybK6LAAAAACo1QiLSrHreJpunr5MP/2aqL8P6aB/jehEfyKgBhvStbHeGddTh0+e1rAZy7XreJrVJQEAAABArUVYdI6vNh/TkNeWKz07X3ETe2ts76YyDMPqsgCcR79WoZo3qbdyXaZGzFyhtfuTrS4JAAAAAGolwqIC+S5T//p6h6bM3aDWEQH68r5+6tEs2OqyAFyA9pGBWjAlVqEB3hrz1mp9veWY1SUBAAAAQK1DWCQpJTNX4+es1YylezSqRxPNm9RbDQMdVpcF4DdoEuyrTyfHqkOkU1PmbtB7K/dbXRIAAAAA1CrnDYsMw5htGMYJwzC2lHG8jWEYKw3DyDYM44HKL7Fq7YxP0+DXlmnFnkQ9PbSjnhneieW3gVqugZ+X5k7orWvaROixRVv1r693yDRNq8sCAAAAgFqhIiOL5kgaWM7xZEm/k/R8ZRRUnRb/ckxDZyxXZk6+PprYW7f1ira6JACVxMfLrlm3d9NtvaI1Y+ke/Wn+z8rNd1ldFgAAAADUeB7nO8E0zR8Nw2hWzvETkk4YhnFjJdZVpfJdpp5bslOzftijbtFBmnl7d0U4mXYG1DUedpueGtJBDZ0OvfjNLiWkZWvm7d3l733eX30AAAAAUG9Va88iwzAmGoaxzjCMdQkJCdX51oVOZeZo3DtrNOuHPRrTK1ofTexDUATUYYZh6HfXtNK/hnfSij1JGvXGSp1Iy7K6LAAAAACosao1LDJN8w3TNGNM04wJCwurzreWJG07mqqbpi/T6r3JemZYRz01tKO8POjVndZDAAAgAElEQVTxDdQHI3s00Vt3xGjPiQwNn7lCexPSrS4JAAAAAGqkepOUfP7zUQ2buVw5eS7Nm9Rbo3rSnwiob65qE664ib2VkZ2vEbNWauPBk1aXBAAAAAA1Tp0Pi/LyXXr6P9v1u7iN6tg4UF/c109doxtYXRYAi3RpEqQFU2Ll7+2h0W+u0v+2H7e6JAAAAACoUc4bFhmGESdppaRLDcM4bBjG3YZhTDYMY3LB8YaGYRyW9EdJ/1dwjrNqy66Y5Iwc3fnOGr3x416N7d1Ucyf0VngA/YmA+q5ZqJ8+nRKr1hEBuue9dfpozUGrSwIAAACAGqMiq6GNPs/xeElRlVZRJdlyJEWT3l+vhLRs/Wt4J43s0cTqkgDUIGEB3oq7p7emzt2gPy/YrPjULN1/TSsZhmF1aQAAAABgqTo5DW3RpiMaMWuF8l2mPp7ch6AIQKn8vD301p0xGtE9Si99+6seWbBZefkuq8sCAAAAAEudd2RRbZKX79I/v9qht5ftU8/mwXrttm4KC/C2uiwANZin3abnRnRSQ6dD07/frYS0bL16W1f5etWpX48AAAAAUGF1ZmRRUnq2xr69Rm8v26dxsc00d0IvgiIAFWIYhh4YcKn+PqSDvt95Qre9uVpJ6dlWlwUAAAAAlqgTYdGWIykaPH251h88qedv6azHB7eXp71OfDQA1Whs76aaeXt3bT+WqhGzVupQcqbVJQEAAABAtav1icqCDYc1fOYKmaapTyfHakT3GtdrG0AtMqB9Q82d0EvJGTkaOmOFthxJsbokAAAAAKhWtTYsys136fHPt+qPH/+srtFB+uK+fuoYFWh1WQDqgJhmwfp0Sh95e9h06+sr9eOuBKtLAgAAAIBqUyvDosT0bI15a7XmrNivu/s11wd391KIP/2JAFSeS8IDtGBqrKJD/DR+zlot2HDY6pIAAAAAoFrUurDo50OndNOry/TzoVP6962d9eigdvKgPxGAKhDhdGjepN7q2TxYf/z4Z81cukemaVpdFgAAAABUqVqVssxfd0i3vL5SNsPQp1NiNbQr/YkAVC2nw1Nz7uqpwZ0j9ezXO/T451uV7yIwAgAAAFB3eVhdQEXk5Ln0j8Xb9N7KA4ptGaLpt3VTsJ+X1WUBqCe8PGx66dYuinB6682f9ul4arZeGtVFDk+71aUBAAAAQKWr8SOLEtKyNeatVXpv5QHdc1lzvTe+J0ERgGpnsxn6643t9Oigdvp6a7we+uQXpqQBAAAAqJNq9MiijQdPasoHG3TqdI5eHtVFN3dpbHVJAOq5u/s1V1Zuvp5bslPNQ/30h/6trS4JAAAAACpVjQ2L5q09qEcXblW401sLpvRVu0in1SUBgCRp6pUttS8xQy//71c1D/XTkK4E2QAAAADqjhoXFuXkufTEF1s1d/VBXdYqVK+M6qoGTDsDUIMYhqGnh3bUoeRMPfTJL4pq4KOYZsFWlwUAAAAAlaJG9Sw6kZql0W+u0tzVBzXpihZ6Z1wPgiIANZKXh02zbu+uxg18NPH99TqYlGl1SQAAAABQKWpMWLT+wEkNenWZth1N1fTbuuqR69vKw15jygOAEhr4eWn2uB7Kd5m6a84apZzOtbokAAAAALhoNSKN+XD1QY16Y6UcnnYtmBqrQZ0irS4JACqkeaifXh/bXQeTM3Xv3A3KzXdZXRIAAAAAXBRLw6LsvHw9suAX/eWzzerTMlSfT+urto1oZA2gdundIkRPD+2oZbsT9diirTJN0+qSAAAAAOA3s6zBdW6+qVFvrNLGg6c09cqW+tN1l8puM6wqBwAuyi0xTbQvMUMzlu5RyzA/TbishdUlAQAAAMBvYllYtPtEmnLj0zRjTDfd0LGRVWUAQKV54LpLtT8pQ0/9Z7uig311XfuGVpcEAAAAABfMsmloNsPQZ1P7EhQBqDNsNkMv3NJFnRoH6v6PNmnLkRSrSwIAAACAC2ZZWHRJhL8ubRhg1dsDQJXw8bLrzTtj1MDXU3e/u1bxKVlWlwQAAAAAF8SysMhu0J8IQN0UHuDQ2+N6KD0rT3e/u1aZOXlWlwQAAAAAFWbpamgAUFe1beTUq7d11fZjqbr/o03Kd7FCGgAAAIDagbAIAKrI1W0i9Oigdvpm23E9+/UOq8sBAAAAgAqxbDU0AKgPxsU2077EDL3x4141D/XT6J7RVpcEAAAAAOUiLAKAKmQYhh4b1E4HkjL16MItig72Vd9LQq0uCwAAAADKxDQ0AKhiHnabpt/WVS3D/DX5g/XafSLN6pIAAAAAoEyERQBQDQIcnnp7XIy8PWwaP2edkjNyrC4JAAAAAEpFWAQA1SSqga/euCNGx1OzNPG9dcrOy7e6JAAAAAAogbAIAKpRt+gGemFkZ607cFJ//nSzTNO0uiQAAAAAKIYG1wBQzQZ1itT+xAw9/99dah7qp99d08rqkgAAAACgEGERAFjg3qsu0d7EDL34zS41C/XT4M6RVpcEAAAAAJKYhgYAljAMQ/8c1lE9mwXrgfk/a/2Bk1aXBAAAAACSCIsAwDLeHnbNGttdjQIdmvjeOh1KzrS6JAAAAAAgLAIAKwX7eWn2uB7KzXdp/Jy1Ss3KtbokAAAAAPUcYREAWKxlmL9m3d5d+xIzdO/cDcrLd1ldEgAAAIB6jLAIAGqA2EtC9dTQDvrp10Q9/sVWmaZpdUkAAAAA6ilWQwOAGuLWHtHam5ih13/Yqxah/hrfr7nVJQEAAACohwiLAKAGeXhAG+1PzNDfF29T0xBfXdM2wuqSAAAAANQzTEMDgBrEZjP071u7qENkoO6L26htR1OtLgkAAABAPUNYBAA1jK+Xh966M0aBPp66+921OpGaZXVJAAAAAOoRwiIAqIEinA69dWeMUk7n6u531ykzJ8/qkgAAAADUE4RFAFBDtY8M1CujumrL0RT9cd7PcrlYIQ0AAABA1SMsAoAa7Np2Efq/G9vp663x+teSnVaXAwAAAKAeYDU0AKjhxvdtpr0J6Zr1wx41D/XVrT2irS4JAAAAQB1GWAQANZxhGHp8cHsdTM7UXz/boibBvoptGWp1WQAAAADqKKahAUAt4Gm36bUx3dQ81E+T31+vPQnpVpcEAAAAoI4iLAKAWsLp8NTscT3kabdp/Jy1Ss7IsbokAAAAAHWQdWFRToaUnWbZ2wNAbdQk2Fdv3NFdx1KyNPn99crOy7e6JAAAAAB1jGGa1izFHBNpN9dN9JeCW0gNOxZsndyPAY0kw7CkLgCoDRZtOqL7P9qkYd0a64VbOsvgdyYAAAAASYZhrDdNM+Zi7nHeBteGYcyWNEjSCdM0O5Ry3JD0sqQbJGVKGmea5obzvnNwS+nqe6T4ze5t26Kzx3xDSgZIIa0kO/24AUCSbu7SWPsTM/Xvb3epZZi/7r3qEqtLAgAAAFBHVCR9mSNpuqT3yjh+vaRWBVsvSTMLHsvncEqXP3j2dXaadHxrQXj0i/tx9RtSfrb7uN1bimhXPECKaC95B1TgIwBA3fO7ay7RvsR0Pbdkp5qG+GpQp0irSwIAAABQB5w3LDJN80fDMJqVc8rNkt4z3fPZVhmGEWQYRiPTNI9dUCXeAVJ0b/d2Rn6elPRr8QBpx2JpQ5HcimlsAOopwzD0zPBOOnzytP708c9qHOSjrtENrC4LAAAAQC1XoZ5FBWHRl2VMQ/tS0jOmaS4reP0/SQ+bprmulHMnSpooSdHR0d0PHDhw4RWbppR2rHiAFL9ZSt579hymsQGoR5LSszV0xgpl5uRp4b19FdXA1+qSAAAAAFikWnoWVSbTNN+Q9IYkxcTE/LbO2oYhOSPdW+sBZ/czjQ1APRXi763Z42I0dMYK3T1nnT6Z0kcBDk+rywIAAABQS1VGWHREUpMir6MK9lWvik5j2/4l09gA1DmXhAdo5pjuuvOdNZr24Ua9fWeMPOw2q8sCAAAAUAtVRlj0uaRphmF8JHdj65QL7ldUVeweUnhb99ZppHtfWdPYWI0NQC3Xr1Wo/jGkgx5ZsFl//3Kbnri5xMxhAAAAADiv86YfhmHESbpSUqhhGIcl/U2SpySZpjlL0n8k3SBpt6RMSXdVVbGVgmlsAOqw0T2jtTchXW/+tE/NQ/00rm9zq0sCAAAAUMtUqMF1VYiJiTHXrSvRA7tmKW0a27FfpNPJZ89hGhuAGibfZWryB+v1v+3H9fadPXRVm3CrSwIAAABQTSqjwTVh0YViNTYAtUBmTp5umbVS+xMz9MmUWLVt5LS6JAAAAADVgLCoJiltGtvxbUxjA2CZ+JQs3fzaMtkNQwun9VV4gMPqkgAAAABUMcKimo5pbAAstuVIim6ZtVKtI/z10cQ+8vGyW10SAAAAgCpEWFQbMY0NQDX779Z4Tfpgva7v0FDTR3eTzUYYDQAAANRVhEV1SVaqdGIb09gAVIk3f9yrp/6zXfde1VIPDmhjdTkAAAAAqkhlhEUMVakpHE4purd7O6O0aWzbv5Q2vHf2HKaxAaiACZc1197EdL32/R41C/HTLTFNrC4JAAAAQA1FWFST2T2k8LburdNI976yprFtW3T2OqaxATiHYRh68uYOOpicqb98tllNgn3Vu0WI1WUBAAAAqIGYhlZXMI0NQAWknM7VsBnLlZSRo8+m9lXzUD+rSwIAAABQiehZhPJd6GpsER2lBs0kZ6R7WhyAOulAUoaGzlihQB9PfTY1VkG+XlaXBAAAAKCSEBbhwlVkNTZJ8na6QyNn47OPgUWeOxsTKAG12Lr9ybrtzdXqGh2k9+/uJS8Pm9UlAQAAAKgEhEWoPFmp0ontUsohKfWIlHpUSjnsfkw9IqWfkHTOz4pXgDs8OjdEOhMwBTZ2h0402wZqpIUbj+j38zbplu5R+teITjL4fxUAAACo9VgNDZXH4ZSie0nqVfrxvBwpPV5KOXI2TEot8vz4Nin9uEoGSv7nBElnwqUiAZMjkEAJsMCQro21NzFDr/zvVzUP89PUKy+xuiQAAAAANQBhESrGw0sKinZvZcnPldLii4dIRcOlPf9zHz83UPL0Kz1EKhouOYIIlIAq8IdrW2l/Yob+9fVONQvx0w0dG1ldEgAAAACLERah8tg9paAm7q0s+bnuEUhljVDa8717BJPpKn6dp+/5Ryj5NCBQAi6QYRj614hOOnwyU3+Yt0mNg3zUuUmQ1WUBAAAAsBA9i1Dz5Oe5A6WyRiilHnE36T43UPLwOc8IpSgCJaAMienZGvLacmXnubTw3r5qHORjdUkAAAAAfgMaXKP+ys+TMk6UPUIp5UyglF/8Og/HeUYoRUm+wQRKqJd2HU/T8Bkr1LiBjz6ZEit/bwafAgAAALUNYRFQHle+exW3YiHSmRXeioxQcuUVv87ufXYkkjOy9BFKviEESqiTftyVoLvmrNXlrUL15h0x8rDbrC4JAAAAwAUgLAIulitfykgoMkLp3KlvR6W0o2UHSmX1T3I2dgdKNv6ijdrn/VUH9OjCLRoX20yPD25vdTkAAAAALkBlhEXMMUD9ZrNLAQ3dm7qXfo7L5Z7yVlb/pEOrpK3HJFdu8evsXsUDpdKmvvmGEiihxhnbu6n2JWRo9vJ9ahnmp7F9mlldEgAAAIBqRFgEnI/NdjZQalxeoJRQdv+kQ2vcz0sLlAIalT9CyS+MQAnV7q83ttWBpAw9/sU2NQn21ZWXhltdEgAAAIBqwjQ0oLq4XFJmYtkjlM48z88pfp3NQ/ILlwIi3MGSf4Q7uCr22MgdKtnJf1F5MrLzNGLWSh1KztSnU2J1acMAq0sCAAAAcB70LALqGtOUMhLPCZGOSunHpbT4s4+ZiaVcbLgDo4AIyb9hKeFSwT7/CMnDu9o/Gmqno6dOa8hry+Vpt2nhvX0VFsDPDgAAAFCTERYB9VVejruPUtpxKT2+SJB0rMi+4+5zTFfJ632CSxmdVGSU0pnAycu3+j8bapzNh1N0y+sr1KahUx9N7C2Hp93qkgAAAACUgQbXQH3l4SUFRrm38rjy3SOV0o6VHJ105jHxV/fzc/spSZK382yQVCxUalhkBFNDyTtAMoyq+aywXMeoQL10a1dNmbteD8z/Wa+M6iqbjf/eAAAAQF1FWATUZTZ7wXS0iPLPc7mk0yfLH6V0eK37Me90yes9fUuOUiotVPJpQKhUSw3s0FAPD2yjZ77aoeahfvrTdZdaXRIAAACAKkJYBMC92ppfiHuLaF/2eaYpZaWUMUqpIFw6vkXa/T8pJ63k9Xav4r2TigZKRfsr+YayAlwNNOnyFtqXkKFXv9utZiF+Gt79PCPbAAAAANRKhEUAKs4wJJ8g9xZ2npElORnuICkt/uzopKKPSXuk/cukrFOlvI9d8g8v2UPp3ObdfuGsAFeNDMPQ34d00MHkTP15wS9qEuyrns2DrS4LAAAAQCWjwTUAa+VmuUcmlTZKqWi4lJEo6dzfV4bkF1pyultpzbtZAa7SpGTmaujM5TqZkaPPpvZVs1A/q0sCAAAAUIDV0ADUH/m5UvqJ0kcppRXptZR+QjLzS17v06Dk6KTSwiUvgo+K2J+YoSEzlivY10ufTe2rQF9Pq0sCAAAAIMIiACjJlS9lJpUxBe7MyKWC1/k5Ja/3CijZQ6nYY8G0OG9nvW/WvXpvkm5/e7Vimgbr3fE95eVBnykAAADAaoRFAPBbmaZ7BbjSAqVzm3fnZpa83u4t+YUVTIMLP/vcL7zkft8QyV43R958uv6w/jT/Z90a00TPDO8oo54HaAAAAIDVKiMsojMsgPrJMCTfYPcW0a7s80xTyk4rGSplJLj7KKWfcG/Ht0kZJ0ofrSRJPsEFIVKY5B929nnR7cx+L/9aM2ppePco7UvM0PTvd6tFmJ8mXdHS6pIAAAAAXCTCIgAoj2FIDqd7C2td/rmmKWWnng2RMhJKbukJUvwW9/PSVoKTJA9HwQil0FLCpaL7w90hlMUrwv2xf2vtS8rQM1/vUNMQPw3s0NDSegAAAABcHMIiAKgshiE5At1bSAVG2OTlSJmJZ0OkwlDphDtwykhwrwwXv9n93JVb2pu6R0edGyKdeX5uwOTtX+kf22Yz9MItnXXk5Gn9ft5GzQ+KVceowEp/HwAAAADVg55FAFAbmKaUlXLOKKUioVLRgCk9QcpOKf0+nr5FgqRzA6Zz+i75Bks2e4VLTEjL1pDXlis336VF0/qqUaBPJX14AAAAABVFg2sAQOnysgvCo6Ih0omzvZYyij5PkFx5Je9h2NzNuctr3l108/LVzvg0DZ+5QtHBvpo/uY/8vBnACgAAAFQnGlwDAErn4S0FNnZv5+NyufsnnRsindt36egG9/7s1NLv4+mnS/3DtDw0SGtO2LVueoQu69xONv9S+i75NJBstsr9zAAAAAAqBWERANR3NtvZleHO18RbknJPF5n+VjJgCsxIUPeco8pL3SMtWyIpv+Q9DLt71FKx/krl9F3yZEobAAAAUF0IiwAAF8bTRwpq4t7KECzp8c+36t0Ve/XsDdEa2ca77NXhMhKkk+vcjznppd/QK6CMIOmcUMk3xD1qyeIV4gAAAIDajD9NAwCqxKOD2ulAUoYe+fqIGjbsoctbtzn/RTmZ7hXiylodLv2ElLxPOrTGfZ7pKv0+jkB3cHRm8ykYOVV0X9HXPg0uqJk3AAAAUJfR4BoAUGXSs/M0YuYKHTl5WgumxqpVREDl3dyVL50+eTZEykwq2JLPPj+dXHxfbmYZNzMkn6CCUOncQOncfQXhk08QARMAAABqHFZDAwDUeEdOndbN05fL4WnTwnv7KtTf27pick8XD5OKBkmny9ifl1XGzQz3iKSKBEtnjjuCaOwNAACAKkVYBACoFTYdOqVbX1+p9pFOfXhPbzk8a9GInJzM4iHS6ZPnhEpngqUzYVOilJ9T+r0MWylT4oLPCZXOCZ+8AwmYAAAAUGGERQCAWuM/m49p6twNGtw5Ui+P6iLDMKwuqWqYppSTUfo0uPKeu3JLv59hL9lf6dzRS77BxR+9nVJd/X4BAABQrsoIi2hwDQCoFjd0bKQHB1yq55bsVPNQP/2hf2urS6oahiF5+7u3Bk0rdo1puleCKxEilTI1LnmvdHhtQcCUV/r9bB7njFQ6J0wquv/Med4BBEwAAACQRFgEAKhGU69sqX2JGXr5f7+qeaifhnRtbHVJNYNhuMMa7wCpQbOKXWOaUnZqQYhU2tS4MyObkqXEXWfDJjO/9PvZPMvvv+RTSuDk5UfABAAAUAcRFgEAqo1hGHp6aEcdSs7UQ5/8oqgGPoppFmx1WbWTYUiOQPdW0a/Q5SoSMJW2alyR/Se2u5+fTpZMV+n3s3udp/dSsHva3JnNEeSu184fPwAAAGoyehYBAKrdyYwcDZu5Qimnc7Vwal9Fh/haXRLK4nJJWafKWTWulJFNp09KKufPF95OySfIHR75NHA/LxooFd1X9LWXPyOZAAAAzoMG1wCAWmtfYoaGvLZcof5eWjC1rwJ9PK0uCZXFlS9lpUgZie6g6fQpd4CUVfB45nVp+8pq9C0V9GI6T6BUWujkCJI8vKrv8wMAAFiIsAgAUKut2puksW+vVq/mIXrnrh7ytLNEfL1mmlJu5vkDpRKh0ykpO6X8e3v6nRMolTKyqbTQyStAsvFzCQAAag9WQwMA1Gq9W4To6aEd9eAnv+ixRVv19NAOMphmVH8ZhrtptpefFBh1YdeeGc10JjzKOidMOjdkStx99nVeVjk12QpCpDICJUeRYOncfZ6Oi/s+AAAALFKhsMgwjIGSXpZkl/SWaZrPnHO8qaTZksIkJUu63TTNw5VcKwCgDrolpon2JWZoxtI9ahnmpwmXtbC6JNRGNvvZVdwuVO7pskctlTZ17uS+gmMpZTf/liQPx3kCpaKjnIoeC3R/HgAAAIucNywyDMMu6TVJ/SUdlrTWMIzPTdPcVuS05yW9Z5rmu4ZhXC3pn5LGVkXBAIC654HrLtX+pAw99Z/tig721XXtG1pdEuoTTx/35mx0YdedWV2uIr2YslKkUwekYz+7X+dmlH9vR+CFjWI689rTlybgAADgolVkZFFPSbtN09wrSYZhfCTpZklFw6J2kv5Y8Px7SQsrs0gAQN1msxl64ZYuOnJype7/aJPmT+6jDo0DrS4LFZSWlau9CRnafSJdexLSCx/jU7LULNRP7Ro51T7SqXaRgWrTKEBORx1pZm6zFYQ0QVKDZhd2bV7O+UcxZRUJn1KOnN3nyiunJs/zB0qOwLObt7PguZP+TNUsN9+lPQnp2n4sVUdPZSkyyKHoYF9FB/sp1N+LKbkAAEudt8G1YRgjJA00TXNCweuxknqZpjmtyDkfSlptmubLhmEMk/SppFDTNJPOuddESRMlKTo6uvuBAwcq9cMAAGq3E2lZGjJ9ufJNU4vu7aeGgfR8qSlM09SJtGztOZGu3QnpRR4zFJ96tuePh81Qs1A/tQzzU0OnQ3sTM7TtaKqSMnIKz4kO9i0SILm3hk4HfzmuCNOUctJLD5TKDZ1OuUdBlcsoHh4VC5NK23fmdZHjHt7V8jXUNiczcrT9WKq2HUvV9mNp2n4sVbtPpCsnv/RpjH5edjUJ9lXTEF81DfFT9JnnwX6KDHLIg8UAAADlqJbV0CoYFkVKmi6puaQfJQ2X1ME0zVNl3ZfV0AAApdl+LFUjZq5Qs1A/fTypj/y8WYuhOuXmu3QwObNYGLQ7IV17T6QrLfvsiBZ/bw+1DPdXyzA/XRLur0vC/NUy3F/Rwb4lVrUzTVMJadnaeixV244WbMdStS/x7FSsBr6e7uCokVPtIwPVLtKpFqF+/KW4MuXnuYOjrBT3lp169nlW6jn7zrxOKXJ+Wvk9miR3n6ZSA6Wir4PKDqG8/Gv16KZ8l6l9iRnafiy1yJZWLFANC/BW20ZOtW0UoHaNnGrbyKmoBj46lpKlg0mZOpCUoQPJme7nyZk6mJypnLyz37vdZqhxkI+ahvgWhkjRwX6Fr/mdCQCorrCoj6THTdMcUPD6EUkyTfOfZZzvL2mHaZrlLmNCWAQAKMt3O45rwrvrdE3bCM26vbvsNkacVLb07DztLTJlzP2YoQNJGcrNP/tng4ZOh1qG+xWGQWcewwO8L3okUHp2nnbGp2prkQBpR3xa4V+MvTxsatMwoNgopDYNnfxl2Coul3tUU4lA6czrU+WETgWP5a08J7lXn/MOKDliqbRpcyVGPhW89vCqlq8jNStXOwpGCZ3Zdh5PU1au++fXw2boknD/wmCobUEwFOp/YaOvXC5Tx9OydCDJHSAdTC4IkQpCpVOZucXOD/X3VnSwT/ERSSFMbwOA+qS6wiIPSbskXSPpiKS1km4zTXNrkXNCJSWbpukyDOMpSfmmaT5W3n0JiwAA5Xln+T498cU2Tby8hf5yQ1ury6mVzozoOTNtbE+RvkLHUopPHWsa4quWYf66JNy/8LFFmJ8Cqrm/UF6+q3Dq2tb/b+/Ow6Qqz/SP30/v1Xs3NNBAAy4g4AbSuGYxk200i+IWcQuucc0yZjLJmF9iMr8kZhLHMSqKGtEY467RSSYxJtEsooZFQFFUkH1fet+Xd/44p6qrm96AqjrVXd/PdfXVVeecqnrgUFTVXe/zvltr9PY2L0wKfyA2kyaN8OZBCrewHVleqLIYhFdIgPaWXkYt9QyY+hvpVCup//euyggN0DbXS8AUPcopK7/bJOGdnU6bqhr9NrKucGhzVVPkmJLczEgYFA6HDh+Vr+yM+K9qV9PU5o9CaogEShv2NmjjnkZtq21W9Fv9nu1tFaW5muhfH1ccYiQfAAwTCQmL/Ac6XdJ/S0qX9IBz7gdm9n1JS5xzz/utaj+S9+r9V0nXOeda+rtPwiIAQH+cc/ru86v0i1c36EdnHa25x08IuqSk1R5uHetlkum65h6tY2V5OswfHRQOhSaO2Ld1LJk457S9ttkPkLpGIW3c2xg5ZmR+ltGV8eIAACAASURBVKZFtbBNLy/UISPzGJU23HR2Sq11g2ydiwqfooOojtZ+H8JZmloz8tVgearuDGl3W46qO0OqVZ7qXEjpucXKKxqhkpKRKisbpXFjRquktEwWbrHLKZTSk2MS95b2Dm2uaqK9DQBSTMLConggLAIADKS9o1OXP7REr6zZrYcuO16nHD4y6JIC1dDS7q06tqtOa3c2REKh9T1ax0YXZnuBUI+RQqMLh9fom3Ab0KqtNZEA6b0ddZG/i5zMNE0d448+8gOkqWMKFcqK/2gPJLG2ZrnmGu3YtUMbtmzXth07tGvXTlVX7VZ7Y5Xy1aRCNag0o1ljs1s1IqNZxWmNyu1sUGZ7naylbuDHyMzd/8nBswu6frIKpPT4hjPd2tuiQ6Q+29uy/BCJ9jYASHaERQCAYa+uuU3n3P2qttY06dlrT9bhowqCLimunHPaVd8SmVh6rT9CaO3Oem2Nah1L76d1bNgsTX8AWtu95cgjo5C2eUFSrT/CKs2kQ0bmafrYom5zIe3vPDIYOprbOvTejrrIZNNvb6vV6m1d/yYkaeKIXE0bU9htfqHxJaHeA5DODm+y7/7mZor+6XlMc43U2bbv/faUEeoeIPX8ycr3L0cHTT22ZeVLWXnd2uoGq2d726a9jZFgaWtNU7f2ttysdE0ojRqRNCIv0t42tjiU1CMXAWA4IiwCAKSEzVWNOvOuV5SblaFfX3eKSvMSM4FtPLV3dGpTVVMvS9HXd/sQm5eV3q1lLLz62ITSPGVl8AFsMJxz2lLdFBUgea1sW6q75pwZVZC9z2psE0tzlUYb25DhnNPOuhZ/efquJeo/2FWvTv/tbm5Wuo4Y0zXZ9PTyAh0xplD5iWyxcs6b6Ltn61xLndRS7/+u80Km1ujr4f21XdcHEzpZmjdSqVuY1GMU04ChlB8++ZOHH2h7W/QcSeEWN9rbACD2CIsAAClj2cYqzb33NR09rkiPXHlCQiaOjYXGVq91bG2PlcfW725Ua0fXB6qygmx/pbGolcdG5WtMYQ7tHXFS09jmT6BdEwmQ1uysV7ufLORmpfuBQlcr25TRBcrJHBr/9oaz1vZOrdlZ37US2XYvHNrb0DUf0bjiUCQQmlZeqKnlwzAAbG/pCpa6BUpR21p7BFAtPQKo8P6BJg6XpPTsvkcw+eFSZ1aB6l2OdrVlaXtLlrY2ZmhDQ7rW15reqzFtbcpQg3Lk5IXdPdvbukYn5aosf3i1zgJAohAWAQBSym9WbtX1v3pDc2aO03+dd2zSfIhwzml3fWu3QGjtrgat3VnfbfRKmkkTR4QnmO4KhQ4ry1dRKHVbx5JJS3uH3t9RHwmPwnMh1bd4o73S00yHleVFBUhFmlZeOCxGuyWrPfUtkVFC3opktVq7qz4yN1V2Rpo3WmhMVwvZ1PJCnlP7o7NTamsYIGyq1b6jn3oZAdXePPDjSWrPyFVzWp4aLVe1nTmq6sjRnrYs1SukOhdSvUJqTctVVl6RQvnFKiwuUXHxCI0cMUKjy8o0qqxMmaEiKSP7gNrsAGA4IywCAKScO//8vn76h/f0L5+coi9/fHJCH7uj02nT3sZ9Rgmt3dWgmqaudpBQZnpXGBRuH/NXHRsqI6LQpbPTaXNVk97eVtNtNbZtUXNIlRfldBuBNL28SBWlfcx5g161d3Rq3e4Gv42sKxzaWde1wO7owuxuS9RPLy/QpBF5LPmeTDraeoRJ4cBp4NFPnc216mjywqf0tnqluY4BH65d6WpNz1N7pje6KSOnUFl5RcoIFfbTWtdzW35CJhUHgEQhLAIApBznnG58coWeWbZFP5s7U58/dmzMH6OptcMfHRSeYNpbkn7d7oZurWMj87N1+Ki8fSaZHlOYM7xaXdCrvQ2teifcxrY1POKlQR1+G1tBdoYXaIztamWbPDqfwFBeC6DXOtY1v9B7O+rU4s91k5luOnxUgaaVF2h6VDjECK4U4pzU1hQJmDqb61RdvUe7d+9WVfUe1dVUq7GuSi0NNepoqlFmR6Py1eT9WJOK0ppVmNasPNeobDe40U7eKnbd2+qUlef/5HdNGN5tW54fNuXvuz0jh1FPAAJBWAQASEkt7R26+P5/aPnmaj165YmaNbFkv+/DOae9Da2RkUHRI4V6to5NKM2NhEGRyabL8lWUS5sLuguvvBU9mfY722rV2OqNkMhIMx0+Kr/7ZNrlhcP231Jnp9OGvY1RoZAXDEU/x0bkZXVbhWxaeaEOK8tnAnfsl/DqbRv3eiu4eZNve9e31zQo1zVHgqSRmS06pKBTE/I6NC63XWOy2zQyq02lGc3Ktyalh9vqWhu8UU+tDd5oqPD1wczvJEmW3nvAlN1LsNRrINVLGEUABWAQCIsAAClrb0Or5sx/RfXN7fr1daeoojS31+M6Op02V0W1ju30J5veVa/qxu6tY4f6K41FjxSaNJLWMRyccGDiBUhdk2lHt1eNKw5FBUjeKKRxxUOrja2+pV3vbq/V21EtZO9ur4sEZelppkNH5kW1kXmjhsoKmMQY8dVz9baNe5u0cW9DJExq6bF629jiHE0szdMFJ0zQ6UeXd7+z8Iin1gapNRwoRYVKkWCpvpd9PX+Hjx/kBOOSv7pdfo+gqUcgtc+IqAHCqMwQARQwzBAWAQBS2tpd9Zpz1ysaXZijR648QbvqWrqNElq7s14f7G7otozzyPwsHdqjbeywsjyNLQrROoaE2lXX4rex1foBUo0+2N2g8FuzwpwMP0AqisyFdPiofGUGPD+Pc+E5nLxAaPW2Or2zvVYb9jRGjinMyYiaV8j7PXl0PivJIel0djrtrGvRhj0N2rC30QuU9jbqrS01Wre7Qf/66SN07amHxTfQ7BZARQdJdd1Dpdb67iOcuu3r5VjXOfBjSz0CqAGCpX3CqJ6BVAEBFJAECIsAAClv0ZrduuSBf0SWO5e81rGK0txuYVA4HCrOZc4TJK/G1na9u70uKkCq1erttWpu8z70ZaWnafLo/G6rsU0tL1BhTnza2JpaO/TujrpubWSrt9Wpzl8dzkyaNCLPayEb448YGluosUU5jBbCkNbS3qFvPLVSzy3fqrnHV+j7ZxwVeFC7X5zzVqbbr1FO0WFUH8cONoCS9QiY+przqbfRUX206mXmEkABg0RYBACApFfW7NaS9VXeCmSj8jVpRB4jGDBsdHS6yCphkcm0t9ZqT0Nr5JgJpbndV2MbW6gxhYMPbJxz2l7bHJlTKDxqaP3uBoVz2LysdE3tMbfQ1DEFys1iBSkMT52dTre++K7uemmtPjKlTPMvPE752Sn87z0cQPU7yqmvcKq39jx/RNR+BVB+aJSV6/2OXPZHM4UvH8j+9EzCKAwbhEUAAAApyDmnXXUtWuWPPgqvxrZud0PkmJLczG4rsR05tkiHjsxTh3N6f0d9JBh6Z1ut3tle220Or/EloW7L008rL1RFSS6tmkhJj/5jo77967c0ZXSBFs6brTFFOUGXNHz0DKCi53HqLXBqrZfaGqXWRv93g9fC19bQtS28v7Nt4MePZun7GUaFoi4PJoxK4aARCUdYBAAAgIj6lnat3tbVwvb2tlqt3l4XmbcrKyNNnZ0u0raZk5mmI8YUatqYqNFCcWxrA4aql9/dqeseWabCUKYemDdb08oLgy4JA+loiwqTwsFS4+DDpm77o2/vzy/lOvavnrTMfsKm3K65nnoNmwaxP40R1ehCWAQAAIB+tXd06oPdDZEWtqyMtEgwNGlEntIZLQQMyqqtNbrswcVqaOnQ/AuP00emlAVdEoLinNTRuh9h1P7s98Oowa6QF5aRM8iwKbdrBNVgw6iMkJQ2hObsAmERAAAAACTK1uomXfbgYq3ZWa8fzjla582uCLokDEeR9rzo0U49wqSBwqboUCpy2R9B1d60/zVl9jIaKhI2+SFURo6/3Q+uMkL+vtAgr+cwb1SMxCIsonESAAAAAAZhbHFIT159kq59ZJm+8fRKbapq1L98cgqr/yG2zLoCFI2I/f13dnqBUThAamvqHib1DJj6C6Dqd0htzd4x7U1d4dWgJy7vISPkh019hU/97SOYiiXCIgAAAAAYpIIcb96im559U3f8eY02VzXpx2cfo6wM2nQwRKSleW1nWXmS4tBO6Zw3Z1QkPGrqESY1e4FSu/+72/V+jm2uSXAw1VvY1NvxfV0f2sEUYREAAAAA7IfM9DT9+OxjVFGSq1tffE/bapq04KJKFeUyOTwgMykjy/vJKYrvYx1UMNXYS/jk/zTX9H77gwqm9jd82s+RUpm5UkZ2zIIpwiIAAAAA2E9mphs+PlnjS0P6xlMrdfY9i7Rw3mxVlOYGXRqQOoIIpgYzCiqwYMpvYYwBwiIAAAAAOEBzZo7XmMKQvvTwEs2Zv0gPzKvUMeOLgy4LQKxFB1Pxtk8w1U/Y1Nt1/eCgS2A1NAAAAAA4SO/vqNO8hYu1t6FVd8ydqU9MHx10SQBSVCxWQ2MWNgAAAAA4SJNHF+jZ607W4aPyddXDS/SLV9cHXRIAHDDCIgAAAACIgVEFOXr8Syfqn6aO0neeW6Uf/PZtdXYG08kBAAeDsAgAAAAAYiQ3K0MLLq7UJSdN1H1/W6frH12m5raOoMsCgP1CWAQAAAAAMZSeZvre54/Utz8zTb97a7suuO817alvCbosABg0wiIAAAAAiDEz0xUfPlTzLzhOq7bW6qy7F2nd7oagywKAQSEsAgAAAIA4Oe3ocv3qyhNV19yus+a/oiXr9wZdEgAMiLAIAAAAAOJo1sQSPXPNySoKZeqC+1/Xb1duC7okAOgXYREAAAAAxNmkkXl65tpTdPS4Il33q2Va8Je1co6V0gAkJ8IiAAAAAEiA0rwsPXLFCfrM0eX60e9W6/8995baOzqDLgsA9pERdAEAAAAAkCpyMtN1x9yZGl8S0oK/fqCt1c26Y+5M5WXz0QxA8mBkEQAAAAAkUFqa6VunT9N/nHmUXn53p75w76vaWdscdFkAEEFYBAAAAAABuPjEibr/i5X6YFeD5sxfpPd21AVdEgBIIiwCAAAAgMD809TRevyqk9Ta0amz716kRWt2B10SABAWAQAAAECQjh5fpGevPVljCnP0xYX/0NNLNwddEoAUR1gEAAAAAAEbX5Krp645WZUTS3Xjkyt0+x/fl3Mu6LIApCjCIgAAAABIAkWhTD102fE6a+Y43fbH9/SvT61Ua3tn0GUBSEGszwgAAAAASSIrI023nnesKkpzdfuf3tf2mmbNv+g4FeZkBl0agBTCyCIAAAAASCJmpq99cop+cs4xeu2DPTr37le1tbop6LIApBDCIgAAAABIQudWVujBS4/X1uomnXnXK3prS03QJQFIEYRFAAAAAJCkPjR5pJ685iRlpJnOW/CqXlq9M+iSAKQAwiIAAAAASGJTxxTq2etO0aQRebriF0v0yOsbgi4JwDBHWAQAAAAASW50YY6euPokfXjySN307Fu65Xer1dnpgi4LwDBFWAQAAAAAQ0B+dobuv6RSF5wwQff8Za2+8vhyNbd1BF0WgGEoI+gCAAAAAACDk5Geph+ceZQmlObqlt+t1vaaJt17caVK8rKCLg3AMMLIIgAAAAAYQsxMV3/0MN0xd6ZWbKrR2Xcv0sY9jUGXBWAYISwCAAAAgCHoc8eO1SNXnqC9ja2aM/8VLdtYFXRJAIYJwiIAAAAAGKJmTyrV09ecrLzsDM299zX9/q3tQZcEYBggLAIAAACAIeywsnw9c+3JmlZeqGseWaqf/31d0CUBGOIIiwAAAABgiBuZn61HrzxRn5o+Wv/xm7d18/Or1NHpgi4LwBBFWAQAAAAAw0AoK13zL5yly045RA8uWq+rf7lUTa0dQZcFYAgaVFhkZv9sZu+a2Roz+2Yv+yeY2Utm9oaZrTSz02NfKgAAAACgP+lppu98brq++7np+uM7O3T+va9qV11L0GUBGGIGDIvMLF3SXZJOkzRd0lwzm97jsG9LesI5N1PS+ZLmx7pQAAAAAMDgXHrKIVpw0Sy9u6NOZ939itbsrA+6JABDyGBGFh0vaY1z7gPnXKukxySd0eMYJ6nQv1wkaWvsSgQAAAAA7K9PHTlGj111kppaO3T23Yv0+gd7gi4JwBAxmLBonKRNUdc3+9ui3SzpIjPbLOl/Jd3Q2x2Z2VVmtsTMluzatesAygUAAAAADNaMimI9e+0pGpGfpYt//g89t3xL0CUBGAJiNcH1XEkPOufGSzpd0sNmts99O+fudc5VOucqy8rKYvTQAAAAAIC+VJTm6plrTtaMCcX6ymPLdddLa+QcK6UB6NtgwqItkiqiro/3t0W7XNITkuSce1VSjqSRsSgQAAAAAHBwinOz9PDlx+uMGWP1kxfe1beeeVNtHZ1BlwUgSQ0mLFosabKZHWJmWfImsH6+xzEbJX1cksxsmrywiD4zAAAAAEgS2Rnpuu28GbruY4fpscWbdPlDS1Tf0h50WQCS0IBhkXOuXdL1kl6Q9I68Vc9Wmdn3zezz/mE3SrrSzFZIelTSPMe4RgAAAABIKmlppn/99FT96Kyj9cqa3Tr3nle1vaY56LIAJBkLKtOprKx0S5YsCeSxAQAAACDV/eW9Xbr2l0tVGMrUA/Nma1p54cA3ApC0ttU0aemGKn3u2HFLnXOVB3NfGbEqCgAAAAAwdHx0SpmevPpkXfbgYp17z6uaf+Fx+sgUFiIChoKOTqd3t9dp6Ya9WrKhSkvWV2lLdVPM7p+RRQAAAACQwrbVNOnShYu1Zme9fjjnaJ03u2LgGwFIqIaWdi3fVK0l66u0ZMNeLd9YrTp/zrGygmxVTizRrIklqpxUqpkTShhZBAAAAAA4cOVFIT159Um69pFl+sbTK7Vxb6Nu/NQUmVnQpQEpa1tNk5asr9LSDV449M62OnV0OplJU0YV6HMzxqpyYokqJ5aqojQU8+crYREAAAAApLiCHG/eopuefVN3vrRGm6sa9eNzjlF2RnrQpQHDXn8tZTmZaZpRUaxrPnqYZk0q0XETSlQUyox7TYRFAAAAAABlpqfpx2cfo4qSXN364nvaXtusBRdVqig3/h9MgVTSX0vZqIJsVU4q0WUfOkSVE0s0fWyhMtMHXMg+5giLAAAAAACSJDPTDR+frPGlIX3jqZU6+55FWjhvtipKc4MuDRiyBtNSNnuS11I2viT2LWUHggmuAQAAAAD7eHXtHn3p4SXKykjXA/Mqdcz44qBLApJedEvZYj8g6tlSVjmxNK4tZWZ20BNcExYBAAAAAHq1Zmed5i1crD31rbpj7kx9YvrooEsCkspgWspmTSxNaEsZYREAAAAAIK521jXrioeW6K0tNbr580fqkpMmBV0SEJj+WsqOGF3gL18fbEtZLMIi5iwCAAAAAPRpVEGOHrvqRH350Tf0nedWadPeRn3rtGlKSwt+XhUgngZqKZtZUaJrTz1MsyaWaGaCVilLFMIiAAAAAEC/crMytODiSn3/f1bpvr+t0+aqJt32hRnKyUwPujQgZobCKmWJQlgEAAAAABhQeprp5s8fqYrSXP3gf9/Rjvte032XVGpEfnbQpQEHZKCWss/PGBt4S1lQCIsAAAAAAINiZrriw4dqXHFIX318uc66e5EevPR4HTIyL+jSgH51dDqt3l7rBUM9WspCmemaUVE8bFvKDgRhEQAAAABgv5x2dLlGFeboyl8s0VnzX9F9l1SqclJp0GUBET1byt7YWK36Hi1ll3/oEFVOKtG08uHdUnYgWA0NAAAAAHBA1u9u0KUPLtaW6ibddt4MfeaY8qBLQooaCquUJQqroQEAAAAAAjNpZJ6evuZkXfWLJbruV8u0uWqqrvrIocP6gziCR0tZ/BEWAQAAAAAOWGleln55xQm68YkV+tHvVmtTVaNu/tyRyqCtBzHSX0vZ6MJsVU4spaUsxgiLAAAAAAAHJSczXXfMnanxJSEt+OsH2lrdrDvmzlReNh85sf8Gaik7I4VXKUsUnrkAAAAAgIOWlmb61unTNL40V9997i194d5X9cAXZ2tUYU7QpSGJ0VKWnAiLAAAAAAAxc/GJEzWuOEfX/+oNzZm/SAsvna0powuCLgtJgpayoYHV0AAAAAAAMffm5hpd9tBiNbd1aMFFs3Ty4SODLgkBGKilLNxONmtiCS1lMRKL1dAIiwAAAAAAcbGlukmXLvyH1u1u0C1nHaOzZ40PuiTE0WBayionlahyUqlmTihWYQ4tZfEQi7CINjQAAAAAQFyMKw7pyatP1jW/XKobn1yhzVVN+vLHD2f0yDBR39KuFbSUDUuERQAAAACAuCkKZerBS4/XN59Zqdv++J42VTXqh3OOVlYGwUGya2hp15bqJm2uatTmqib/p+vy3oZWSYq0lJ05cywtZcMEYREAAAAAIK6yMtJ067nHqqIkV7f/6X1tq2nS3RfNog0pYI2t7fsEQL2FQWHZGWkaXxLS+JJcHT2uSONKQjpybBEtZcMQYREAAAAAIO7MTF/75BSNLwnpW8+8qXPuXqSFlx6vccWhoEsbthpb27WllxAofHlPjzAoKyoMOmpckcaXhFRRkhvZNjI/i9FCKYKwCAAAAACQMOdWVqi8KKRrfrlUc+56RQ/Mm62jxhUFXdaQdDBh0JF+GDQ+EgaFNDIvW2lphEFgNTQAAAAAQABWb6/VZQsXq7qpTXddcJw+NnVU0CUlnYMJg8IBEGFQ6onFamiERQAAAACAQOyobdZlDy7W6u11+v4ZR+rCEyYGXVJCEQYhHmIRFtGGBgAAAAAIxOjCHD3xpZN0/a+W6aZn39KmvU36xqePGDaBB21iGKoIiwAAAAAAgcnLztB9l1TqO8+v0j1/Wast1U36yTnHKCczPejSBtTU2qEt1Y3aFA6B9jYSBmFYICwCAAAAAAQqIz1NPzjzKE0ozdUtv1ut7TVNuvfiSpXkZQVa1z5hUNTooC1Vjdpd30sYVBzSuJKQPjW2qFurWEVJSCPzCYMwNBAWAQAAAAACZ2a6+qOHaVxxSDc+sUJn371ICy+drYkj8uL2mAcTBk2fPoYwCMMWYREAAAAAIGl87tixGlOUoyt/sURnzV+k+75YqeMmlBzQfREGAQeG1dAAAAAAAEln7a56XbpwsXbUNuv282fqn48as88x+x0GpXtzBo3rZUUxwiAMF7FYDY2wCAAAAACQlHbXt+iKh5ZoxeZqXX7KIUpPN8IgYACxCItoQwMAAAAAJKWR+dl67KoT9bXHl+v+v69TVnqaHwSFNH366H0CoTLCICAmCIsAAAAAAEkrJzNdd180S1UNrSoKZRIGAQlAWAQAAAAASHoleVlBlwCkjLSgCwAAAAAAAEDyICwCAAAAAABABGERAAAAAAAAIgiLAAAAAAAAEEFYBAAAAAAAgAjCIgAAAAAAAEQQFgEAAAAAACCCsAgAAAAAAAARhEUAAAAAAACIICwCAAAAAABABGERAAAAAAAAIgiLAAAAAAAAEGHOuWAe2KxJ0qpAHhz9KZJUE3QR6IZzkpw4L8mHc5J8OCfJifOSfDgnyYnzknw4J8mJ85J8Jjvnig7mDjJiVckBqHfOVQb4+OiFmd3rnLsq6DrQhXOSnDgvyYdzknw4J8mJ85J8OCfJifOSfDgnyYnzknzM7N6DvY8g29CqA3xs9O1/gi4A++CcJCfOS/LhnCQfzkly4rwkH85JcuK8JB/OSXLivCSfgz4nQbahLWFkEQAAAAAAQHIJcmTRQQ+LAgAAAAAAQGwFNrIIAAAAAAAAySfIkUUIgJl1mNnyqJ9J/Rx7qpn9JnHVpSYzc2b2y6jrGWa2i7/74JnZmf75mRp0LamO50lyM7P6oGtA7wY6N2b2spkxLUAC8JqSfMzsJjNbZWYr/ffFJwRdEyQzG29mz5nZ+2a21sxuN7Osfo7/qpnlJrLGVOL/v3Vr1PWvm9nNAZYEdftcv8rMVpjZjWYW03yHsCj1NDnnZkT9rA+6IKhB0lFmFvKvf1LSlv25AzMLcmXD4WyupL/7vwfNzNLjU05KO+jnCQAE7IBeUxAfZnaSpM9KOs45d4ykT0jaFGxVMDOT9IykXzvnJkuaIilf0g/6udlXJREWxU+LpLPMbGTQhaCb8Of6I+W9Lz5N0ndj+QBxD4v4tjH5mVm6mf3EzBb736x8KWp3oZn91szeNbN7Yp1WIuJ/JX3GvzxX0qPhHWZ2vJm9amZvmNkiMzvC3z7PzJ43sz9L+lPiSx7ezCxf0ockXS7pfH/bqWb2196eE2ZWb2a3mtkKSScFV/mwdiDPk7+a2Yyo4/5uZscmtOoU0XM0qpndaWbz/Mvrzex7ZrbMzN5kZEVi9XdukBj9vKb09Zw53cxWm9lSM/sZoyjjolzSbudciyQ553Y757aa2Swz+4v/d/+CmZVLkVF4t/vf5L9lZscHWv3w9U+Smp1zCyXJOdch6WuSLjOzPDP7qf/3v9LMbjCzL0saK+klM3spwLqHs3Z58w1/recOM5tkZn/2z8efzGyCmRWZ2Yao98h5ZrbJzDITXXiqcM7tlHSVpOvN0+fnezP7N/+92Aozu6W/++WDf+oJWVcL2rP+tssl1TjnZkuaLelKMzvE33e8pBskTZd0mKSzEl5xanhM0vlmliPpGEmvR+1bLenDzrmZkr4j6YdR+46TdI5z7qMJqzR1nCHp98659yTtMbNZ/va+nhN5kl53zh3rnPt7wqtNDQfyPPm5pHmSZGZTJOU451YkrGJE2+2cO07S3ZK+HnQxQIL19ZqyD///uAWSTnPOzZJUlqAaU80fJFWY2XtmNt/MPup/mL1D3nurWZIeUPcRLbnOuRmSrvX3IfaOlLQ0eoNzrlbSRklXSJokaYY/GuwR59zPJG2V9DHn3McSXGsquUvShWZW1GP7HZIeCp8PST9zztVIWi4p/Pnks5JecM61JazaFOSc+0BSuqRR6uPzvZmdbnB2mwAACN5JREFUJu/16ATn3LGS/rO/+0xIWGRm+X7SGP5G8Qx/+yQze8fM7jOv1+4P1tVigPiIbkOb42/7lKRLzGy5vA9fIyRN9vf9wzn3gZ/qPyrvWzHEmHNupbwXv7nyRk9EK5L0pJm9Jek2eS+iYS865/YmpMjUM1deOCH/d7htoK/nRIekpxNbYmo5wOfJk5I+638AuEzSgwkpFr15xv+9VN55BFJJX68pvZkq6QPn3Dr/+qP9HIsD5JyrlzRL3rfxuyQ9LulLko6S9KL/vvjbksZH3exR/7Z/lTf6vjihReNUSQucc+2SxHvgxPEDu19I+nKPXSdJ+pV/+WF1vS9+XNIX/Mvn+9eROH19vv+EpIXOuUZp4OdQouY5aZY0xzlXa16v42tm9ry/b7Kkuc65K83sCUlnS/plX3eEuDBJNzjnXui20exUST2Xy2P5vPh5XtJP5b0Qjoja/h+SXnLOzTFvQvKXo/Y1JKi2lGJmpfKGQR9tZk5eSu8k/VZ9Pyea/QAJ8bVfzxPnXKOZvSjvW5Tz5H0wQHy0q/uXUDk99rf4vzuUuPcf8Ax0bhBH/bymPCfOS6D81+2XJb1sZm9Kuk7SKudcX+3kvC+Ov7clnRO9wcwKJU2QtD6IghDx35KWSVo4iGOfl/RD//+/WZL+HM/CIJnZofLeY+1U35/vP70/95moNjST949lpaQ/ShonabS/b51zbrl/mW8bg/GCpGvCfaRmNsXM8vx9x/tD1tLkpcO018TPA5K+55x7s8f2InVN5DsvoRWlrnMkPeycm+icm+Scq5C0TtKHxXMiaAfyPLlf0s8kLXbOVcW3vJS2QdJ0M8v2v23/eNAFIYJzE6y+XlPS1Pt5eVfSoda1Yu0Xet4hDp6ZHWFmk6M2zZD0jqQy8ya/lpllmln0iO4v+Ns/JK/FoyZhBaeOP0nKNbNLpMiiIbfKGxn8gqQvmb+wix9ESFKdpILEl5pa/FEoT8hrcQpbJH8eNkkXSvqbf2y9pMWSbpf0G75QjS8zK5N0j6Q7nXNOfX++f1HSpeavHhj1HOpVosKiC+X1W8/y+3x3qOvbk5ao4/i2MRj3y0vxl/ktHAvUdR4WS7pT3ovnOknP9noPOGjOuc1+33VP/ynpR2b2hnh+JMpc7ftv/Wl/O8+JAB3I88Q5t1RSrQb3TRj2k/+mvcU5t0nem8i3/N9vBFoYODfJo6/XlPPVy3lxzjXJmxPn92a2VN4HYUKJ2MuX9JCZve1/oT1d3px350j6sXkLViyXdHLUbZr915l71P0DM2LE/6A7R9K5Zva+pPfkdan8u7zPLBslrfTPzwX+ze6V93xhguv4u1VS9KpoN8gLH1ZKuljSV6L2PS7pItGCFi/huYhXyRuQ8wdJ3/P39fr53jn3e3mjvpb4LWr9ziFp3vMxfsxbDe0mSYc7524ws4/JG4YWnkD5N865o/xjvy4p3zl3c1yLAoAD4Ldmft0599mga8HgmdlYeW0GU51znQGXM+yYt7rcfc45VgZKMpybocvM8p1z9WZm8iaWfd85d1vQdaUyM3tZ3nuAJUHXAgCJENeRReFvtOTNjF7p9wJfIm/VGgAA4sofxv66pJsIimLPzK6WN+Hrt4OuBd1xboa8K/1vfVfJa7NdEHA9AIAUE9eRRXyjBQAAAAAAMLTEbWQR32gBAAAAAAAMPXGfswgAAAAAAABDR8xGFplZhZm95K8osMrMvuJvLzWzF83sff93ib99qpm9amYt/sTW0fdVbGZPmdlqM3snvHwlAAAAAAAA4itmI4vMrFxSuXNumZkVSFoq6UxJ8yTtdc7dYmbflFTinPs3MxslaaJ/TJVz7qdR9/WQpL855+43syxJuc656pgUCgAAAAAAgD7FbGSRc26bc26Zf7lO0juSxkk6Q9JD/mEPyQuH5Jzb6ZxbLKkt+n7MrEjSRyT93D+ulaAIAAAAAAAgMeIywbWZTZI0U95yxaOdc9v8XdsljR7g5odI2iVpoZm9YWb3m1lePOoEAAAAAABAdzEPi8wsX9LTkr7qnKuN3ue8nreB+t4yJB0n6W7n3ExJDZK+Ges6AQAAAAAAsK+YhkVmlikvKHrEOfeMv3mHP59ReF6jnQPczWZJm51zr/vXn5IXHgEAAAAAACDOYrkamsmbZ+gd59x/Re16XtIX/ctflPRcf/fjnNsuaZOZHeFv+rikt2NVJwAAAAAAAPoWy9XQPiTpb5LelNTpb/53efMWPSFpgqQNks5zzu01szGSlkgq9I+vlzTdOVdrZjMk3S8pS9IHki51zlXFpFAAAAAAAAD0KWZhEQAAAAAAAIa+uKyGBgAAAAAAgKGJsAgAAAAAAAARhEUAAAAAAACIICwCAAAAAABABGERAAAAAAAAIgiLAABASjKzDjNbbmarzGyFmd1oZv2+NzKzSWZ2QaJqBAAACAJhEQAASFVNzrkZzrkjJX1S0mmSvjvAbSZJIiwCAADDmjnngq4BAAAg4cys3jmXH3X9UEmLJY2UNFHSw5Ly/N3XO+cWmdlrkqZJWifpIUk/k3SLpFMlZUu6yzm3IGF/CAAAgDggLAIAACmpZ1jkb6uWdISkOkmdzrlmM5ss6VHnXKWZnSrp6865z/rHXyVplHPu/5tZtqRXJJ3rnFuX0D8MAABADGUEXQAAAEASypR0p5nNkNQhaUofx31K0jFmdo5/vUjSZHkjjwAAAIYkwiIAAABF2tA6JO2UN3fRDknHypvjsbmvm0m6wTn3QkKKBAAASAAmuAYAACnPzMok3SPpTuf16BdJ2uac65R0saR0/9A6SQVRN31B0jVmlunfzxQzyxMAAMAQxsgiAACQqkJmtlxey1m7vAmt/8vfN1/S02Z2iaTfS2rwt6+U1GFmKyQ9KOl2eSukLTMzk7RL0pmJ+gMAAADEAxNcAwAAAAAAIII2NAAAAAAAAEQQFgEAAAAAACCCsAgAAAAAAAARhEUAAAAAAACIICwCAAAAAABABGERAAAAAAAAIgiLAAAAAAAAEEFYBAAAAAAAgIj/A5lIWEntxDx7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test['Mean'].plot(figsize=(20,6),legend=True)\n",
    "predictions.plot(legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate ARIMA model by:\n",
    "### 1. Calculating mean_squared_error and r2_score (below)\n",
    "### 2. Confidence band (90%, 95%, 99%). This is part of the homework. Read more at https://www2.isye.gatech.edu/~yxie77/isye2028/lecture12.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.015185\n",
      "RMSE: 0.123227\n",
      "R-Squared: 0.383235\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "print('MSE: %f'%mean_squared_error(test['Mean'],predictions))\n",
    "print('RMSE: %f'%np.sqrt(mean_squared_error(test['Mean'],predictions)))\n",
    "print('R-Squared: %f'%r2_score(test['Mean'],predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SARIMAX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seasonal Autoregressive Integrated Moving Average, SARIMA or Seasonal ARIMA, is an extension of ARIMA that explicitly supports univariate time series data with a seasonal component.\n",
    "### It adds three new hyperparameters to specify the autoregression (AR), differencing (I) and moving average (MA) for the seasonal component of the series, as well as an additional parameter for the period of the seasonality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/statsmodels/base/model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/statsmodels/base/model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/statsmodels/base/model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/statsmodels/base/model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/statsmodels/base/model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/statsmodels/base/model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/statsmodels/base/model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/statsmodels/base/model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/statsmodels/base/model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/statsmodels/base/model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/pmdarima/arima/_auto_solvers.py:207: ModelFitWarning: Unable to fit ARIMA for order=(2, 1, 2) seasonal_order=(0, 0, 2, 12); data is likely non-stationary. (if you do not want to see these warnings, run with error_action=\"ignore\")\n",
      "  ModelFitWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/statsmodels/base/model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "/anaconda3/lib/python3.6/site-packages/statsmodels/base/model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "stepwise_fit = auto_arima(GCAG['Mean'],start_p=0,start_q=0,max_p=5,max_q=3,seasonal=True,m=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Statespace Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>                  <td>y</td>               <th>  No. Observations:  </th>   <td>1644</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 1, 2)x(0, 0, 2, 12)</td> <th>  Log Likelihood     </th> <td>1563.768</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                   <td>Wed, 08 May 2019</td>        <th>  AIC                </th> <td>-3113.536</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                       <td>11:24:05</td>            <th>  BIC                </th> <td>-3075.706</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                         <td>0</td>               <th>  HQIC               </th> <td>-3099.507</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                             <td> - 1644</td>            <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>               <td>opg</td>              <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th> <td> 7.478e-05</td> <td> 7.01e-05</td> <td>    1.067</td> <td> 0.286</td> <td>-6.26e-05</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>     <td>    0.8519</td> <td>    0.033</td> <td>   26.005</td> <td> 0.000</td> <td>    0.788</td> <td>    0.916</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>     <td>   -1.3280</td> <td>    0.042</td> <td>  -31.743</td> <td> 0.000</td> <td>   -1.410</td> <td>   -1.246</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>     <td>    0.3540</td> <td>    0.035</td> <td>   10.191</td> <td> 0.000</td> <td>    0.286</td> <td>    0.422</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L12</th>  <td>    0.0106</td> <td>    0.021</td> <td>    0.510</td> <td> 0.610</td> <td>   -0.030</td> <td>    0.051</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L24</th>  <td>    0.1060</td> <td>    0.021</td> <td>    5.045</td> <td> 0.000</td> <td>    0.065</td> <td>    0.147</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td>    0.0088</td> <td>    0.000</td> <td>   36.456</td> <td> 0.000</td> <td>    0.008</td> <td>    0.009</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>36.61</td> <th>  Jarque-Bera (JB):  </th> <td>121.85</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.62</td>  <th>  Prob(JB):          </th>  <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.91</td>  <th>  Skew:              </th>  <td>-0.05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.26</td>  <th>  Kurtosis:          </th>  <td>4.33</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 Statespace Model Results                                 \n",
       "==========================================================================================\n",
       "Dep. Variable:                                  y   No. Observations:                 1644\n",
       "Model:             SARIMAX(1, 1, 2)x(0, 0, 2, 12)   Log Likelihood                1563.768\n",
       "Date:                            Wed, 08 May 2019   AIC                          -3113.536\n",
       "Time:                                    11:24:05   BIC                          -3075.706\n",
       "Sample:                                         0   HQIC                         -3099.507\n",
       "                                           - 1644                                         \n",
       "Covariance Type:                              opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept   7.478e-05   7.01e-05      1.067      0.286   -6.26e-05       0.000\n",
       "ar.L1          0.8519      0.033     26.005      0.000       0.788       0.916\n",
       "ma.L1         -1.3280      0.042    -31.743      0.000      -1.410      -1.246\n",
       "ma.L2          0.3540      0.035     10.191      0.000       0.286       0.422\n",
       "ma.S.L12       0.0106      0.021      0.510      0.610      -0.030       0.051\n",
       "ma.S.L24       0.1060      0.021      5.045      0.000       0.065       0.147\n",
       "sigma2         0.0088      0.000     36.456      0.000       0.008       0.009\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                       36.61   Jarque-Bera (JB):               121.85\n",
       "Prob(Q):                              0.62   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               0.91   Skew:                            -0.05\n",
       "Prob(H) (two-sided):                  0.26   Kurtosis:                         4.33\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stepwise_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/statsmodels/base/model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "model = SARIMAX(train['Mean'],order=(1, 1, 2),seasonal_order=(0, 0, 2, 12))\n",
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = len(train)\n",
    "end = len(train) + len(test) - 1\n",
    "predictions = results.predict(start=start,end=end,typ='levels').rename('SARIMA Predictions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c2791c240>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test['Mean'].plot(figsize=(20,6),legend=True)\n",
    "predictions.plot(legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.014975\n",
      "RMSE: 0.122372\n",
      "R-Squared: 0.391760\n"
     ]
    }
   ],
   "source": [
    "print('MSE: %f'%mean_squared_error(test['Mean'],predictions))\n",
    "print('RMSE: %f'%np.sqrt(mean_squared_error(test['Mean'],predictions)))\n",
    "print('R-Squared: %f'%r2_score(test['Mean'],predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FeedForward Neural Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deep Feedforward networks or also known multilayer perceptrons are the foundation of most deep learning models. The main goal of a feedforward network is to approximate some function f*. For example, a regression function y = f *(x) maps an input x to a value y. A feedforward network defines a mapping y = f (x; θ) and learns the value of the parameters θ that result in the best function approximation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#\n",
    "#\n",
    "## Scaling or Normalization of values\n",
    "### Scaling or Normalization is a technique often applied as part of data preparation for machine learning. The goal of normalization is to change the values of numeric columns in the dataset to use a common scale, without distorting differences in the ranges of values or losing information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "sc.fit(train)\n",
    "\n",
    "scaled_train = sc.transform(train)\n",
    "scaled_test = sc.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X_train = scaled_train[:-1]\n",
    "y_train = scaled_train[1:]\n",
    "\n",
    "X_test = scaled_test[:-1]\n",
    "y_test = scaled_test[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1631 samples, validate on 11 samples\n",
      "Epoch 1/100\n",
      "1631/1631 [==============================] - 0s 115us/step - loss: 1.2032 - val_loss: 6.7727\n",
      "Epoch 2/100\n",
      "1631/1631 [==============================] - 0s 29us/step - loss: 0.9456 - val_loss: 5.5489\n",
      "Epoch 3/100\n",
      "1631/1631 [==============================] - 0s 31us/step - loss: 0.7708 - val_loss: 4.1512\n",
      "Epoch 4/100\n",
      "1631/1631 [==============================] - 0s 28us/step - loss: 0.6488 - val_loss: 2.8970\n",
      "Epoch 5/100\n",
      "1631/1631 [==============================] - 0s 30us/step - loss: 0.5660 - val_loss: 1.9082\n",
      "Epoch 6/100\n",
      "1631/1631 [==============================] - 0s 28us/step - loss: 0.5159 - val_loss: 1.2793\n",
      "Epoch 7/100\n",
      "1631/1631 [==============================] - 0s 26us/step - loss: 0.4868 - val_loss: 0.8720\n",
      "Epoch 8/100\n",
      "1631/1631 [==============================] - 0s 23us/step - loss: 0.4694 - val_loss: 0.6099\n",
      "Epoch 9/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4583 - val_loss: 0.4610\n",
      "Epoch 10/100\n",
      "1631/1631 [==============================] - 0s 26us/step - loss: 0.4509 - val_loss: 0.3488\n",
      "Epoch 11/100\n",
      "1631/1631 [==============================] - 0s 28us/step - loss: 0.4461 - val_loss: 0.2742\n",
      "Epoch 12/100\n",
      "1631/1631 [==============================] - 0s 34us/step - loss: 0.4430 - val_loss: 0.2374\n",
      "Epoch 13/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4408 - val_loss: 0.1954\n",
      "Epoch 14/100\n",
      "1631/1631 [==============================] - 0s 23us/step - loss: 0.4394 - val_loss: 0.1715\n",
      "Epoch 15/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4383 - val_loss: 0.1582\n",
      "Epoch 16/100\n",
      "1631/1631 [==============================] - 0s 23us/step - loss: 0.4376 - val_loss: 0.1434\n",
      "Epoch 17/100\n",
      "1631/1631 [==============================] - 0s 27us/step - loss: 0.4371 - val_loss: 0.1351\n",
      "Epoch 18/100\n",
      "1631/1631 [==============================] - 0s 23us/step - loss: 0.4368 - val_loss: 0.1228\n",
      "Epoch 19/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4366 - val_loss: 0.1202\n",
      "Epoch 20/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4365 - val_loss: 0.1139\n",
      "Epoch 21/100\n",
      "1631/1631 [==============================] - 0s 23us/step - loss: 0.4364 - val_loss: 0.1095\n",
      "Epoch 22/100\n",
      "1631/1631 [==============================] - 0s 23us/step - loss: 0.4363 - val_loss: 0.1075\n",
      "Epoch 23/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4362 - val_loss: 0.1077\n",
      "Epoch 24/100\n",
      "1631/1631 [==============================] - 0s 22us/step - loss: 0.4362 - val_loss: 0.1034\n",
      "Epoch 25/100\n",
      "1631/1631 [==============================] - 0s 23us/step - loss: 0.4362 - val_loss: 0.1032\n",
      "Epoch 26/100\n",
      "1631/1631 [==============================] - 0s 23us/step - loss: 0.4362 - val_loss: 0.1025\n",
      "Epoch 27/100\n",
      "1631/1631 [==============================] - 0s 23us/step - loss: 0.4363 - val_loss: 0.1014\n",
      "Epoch 28/100\n",
      "1631/1631 [==============================] - 0s 23us/step - loss: 0.4362 - val_loss: 0.1032\n",
      "Epoch 29/100\n",
      "1631/1631 [==============================] - 0s 23us/step - loss: 0.4363 - val_loss: 0.1009\n",
      "Epoch 30/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4362 - val_loss: 0.1019\n",
      "Epoch 31/100\n",
      "1631/1631 [==============================] - 0s 28us/step - loss: 0.4362 - val_loss: 0.1021\n",
      "Epoch 32/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4362 - val_loss: 0.1037\n",
      "Epoch 33/100\n",
      "1631/1631 [==============================] - 0s 23us/step - loss: 0.4363 - val_loss: 0.1094\n",
      "Epoch 34/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4364 - val_loss: 0.1029\n",
      "Epoch 35/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4362 - val_loss: 0.1006\n",
      "Epoch 36/100\n",
      "1631/1631 [==============================] - 0s 22us/step - loss: 0.4363 - val_loss: 0.1018\n",
      "Epoch 37/100\n",
      "1631/1631 [==============================] - 0s 26us/step - loss: 0.4362 - val_loss: 0.1050\n",
      "Epoch 38/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4362 - val_loss: 0.1013\n",
      "Epoch 39/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4362 - val_loss: 0.1006\n",
      "Epoch 40/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4362 - val_loss: 0.1043\n",
      "Epoch 41/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4362 - val_loss: 0.1034\n",
      "Epoch 42/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4363 - val_loss: 0.1056\n",
      "Epoch 43/100\n",
      "1631/1631 [==============================] - 0s 23us/step - loss: 0.4363 - val_loss: 0.1056\n",
      "Epoch 44/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4362 - val_loss: 0.0989\n",
      "Epoch 45/100\n",
      "1631/1631 [==============================] - 0s 23us/step - loss: 0.4363 - val_loss: 0.1020\n",
      "Epoch 46/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4362 - val_loss: 0.1027\n",
      "Epoch 47/100\n",
      "1631/1631 [==============================] - 0s 27us/step - loss: 0.4363 - val_loss: 0.1083\n",
      "Epoch 48/100\n",
      "1631/1631 [==============================] - 0s 26us/step - loss: 0.4362 - val_loss: 0.1061\n",
      "Epoch 49/100\n",
      "1631/1631 [==============================] - 0s 26us/step - loss: 0.4362 - val_loss: 0.1023\n",
      "Epoch 50/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4363 - val_loss: 0.1003\n",
      "Epoch 51/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4362 - val_loss: 0.1008\n",
      "Epoch 52/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4362 - val_loss: 0.1035\n",
      "Epoch 53/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4362 - val_loss: 0.1049\n",
      "Epoch 54/100\n",
      "1631/1631 [==============================] - 0s 26us/step - loss: 0.4363 - val_loss: 0.0988\n",
      "Epoch 55/100\n",
      "1631/1631 [==============================] - 0s 23us/step - loss: 0.4363 - val_loss: 0.1018\n",
      "Epoch 56/100\n",
      "1631/1631 [==============================] - 0s 22us/step - loss: 0.4363 - val_loss: 0.1042\n",
      "Epoch 57/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4363 - val_loss: 0.1083\n",
      "Epoch 58/100\n",
      "1631/1631 [==============================] - 0s 27us/step - loss: 0.4364 - val_loss: 0.1102\n",
      "Epoch 59/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4362 - val_loss: 0.1009\n",
      "Epoch 60/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4362 - val_loss: 0.1032\n",
      "Epoch 61/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4364 - val_loss: 0.1026\n",
      "Epoch 62/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4363 - val_loss: 0.1042\n",
      "Epoch 63/100\n",
      "1631/1631 [==============================] - 0s 26us/step - loss: 0.4365 - val_loss: 0.0974\n",
      "Epoch 64/100\n",
      "1631/1631 [==============================] - 0s 23us/step - loss: 0.4363 - val_loss: 0.1009\n",
      "Epoch 65/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4363 - val_loss: 0.1036\n",
      "Epoch 66/100\n",
      "1631/1631 [==============================] - 0s 28us/step - loss: 0.4362 - val_loss: 0.1029\n",
      "Epoch 67/100\n",
      "1631/1631 [==============================] - 0s 28us/step - loss: 0.4364 - val_loss: 0.1091\n",
      "Epoch 68/100\n",
      "1631/1631 [==============================] - 0s 28us/step - loss: 0.4362 - val_loss: 0.1042\n",
      "Epoch 69/100\n",
      "1631/1631 [==============================] - 0s 26us/step - loss: 0.4364 - val_loss: 0.1047\n",
      "Epoch 70/100\n",
      "1631/1631 [==============================] - 0s 26us/step - loss: 0.4363 - val_loss: 0.1066\n",
      "Epoch 71/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4362 - val_loss: 0.1034\n",
      "Epoch 72/100\n",
      "1631/1631 [==============================] - 0s 26us/step - loss: 0.4363 - val_loss: 0.1077\n",
      "Epoch 73/100\n",
      "1631/1631 [==============================] - 0s 26us/step - loss: 0.4362 - val_loss: 0.1031\n",
      "Epoch 74/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4363 - val_loss: 0.1120\n",
      "Epoch 75/100\n",
      "1631/1631 [==============================] - 0s 26us/step - loss: 0.4364 - val_loss: 0.0975\n",
      "Epoch 76/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4364 - val_loss: 0.1044\n",
      "Epoch 77/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4363 - val_loss: 0.1044\n",
      "Epoch 78/100\n",
      "1631/1631 [==============================] - 0s 27us/step - loss: 0.4363 - val_loss: 0.1032\n",
      "Epoch 79/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4363 - val_loss: 0.1072\n",
      "Epoch 80/100\n",
      "1631/1631 [==============================] - 0s 27us/step - loss: 0.4363 - val_loss: 0.1089\n",
      "Epoch 81/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4363 - val_loss: 0.1052\n",
      "Epoch 82/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4363 - val_loss: 0.0980\n",
      "Epoch 83/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4363 - val_loss: 0.1031\n",
      "Epoch 84/100\n",
      "1631/1631 [==============================] - 0s 23us/step - loss: 0.4362 - val_loss: 0.1066\n",
      "Epoch 85/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4362 - val_loss: 0.1002\n",
      "Epoch 86/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4364 - val_loss: 0.0989\n",
      "Epoch 87/100\n",
      "1631/1631 [==============================] - 0s 27us/step - loss: 0.4362 - val_loss: 0.1043\n",
      "Epoch 88/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4362 - val_loss: 0.1019\n",
      "Epoch 89/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4362 - val_loss: 0.0994\n",
      "Epoch 90/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4363 - val_loss: 0.1019\n",
      "Epoch 91/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4363 - val_loss: 0.1009\n",
      "Epoch 92/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4363 - val_loss: 0.1020\n",
      "Epoch 93/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4364 - val_loss: 0.1054\n",
      "Epoch 94/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4363 - val_loss: 0.1114\n",
      "Epoch 95/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4363 - val_loss: 0.1062\n",
      "Epoch 96/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4363 - val_loss: 0.1078\n",
      "Epoch 97/100\n",
      "1631/1631 [==============================] - 0s 24us/step - loss: 0.4363 - val_loss: 0.1006\n",
      "Epoch 98/100\n",
      "1631/1631 [==============================] - 0s 26us/step - loss: 0.4362 - val_loss: 0.1071\n",
      "Epoch 99/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4363 - val_loss: 0.1041\n",
      "Epoch 100/100\n",
      "1631/1631 [==============================] - 0s 25us/step - loss: 0.4362 - val_loss: 0.1012\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1c29cc3b00>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,LSTM\n",
    "from keras.optimizers import Adam\n",
    "import keras.backend as K\n",
    "\n",
    "K.clear_session()\n",
    "np.random.seed(7)\n",
    "model = Sequential()\n",
    "model.add(Dense(7, input_dim=1, activation='relu'))\n",
    "model.add(Dense(1, activation='relu'))\n",
    "model.compile(loss='mean_squared_error',optimizer=Adam(lr=0.001))\n",
    "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c2a09d470>]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_test_NN = np.insert(X_test,0,X_train[-1][0])\n",
    "predictions = model.predict(X_test_NN)\n",
    "plt.figure(figsize=(20,6))\n",
    "plt.plot(y_test)\n",
    "plt.plot(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_inv_scaled = sc.inverse_transform(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.011469\n",
      "RMSE: 0.107096\n",
      "R-Squared: 0.534141\n"
     ]
    }
   ],
   "source": [
    "print('MSE: %f'%mean_squared_error(test,predictions_inv_scaled))\n",
    "print('RMSE: %f'%np.sqrt(mean_squared_error(test,predictions_inv_scaled)))\n",
    "print('R-Squared: %f'%r2_score(test,predictions_inv_scaled))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Long Short-Term Memory (LSTM) networks are a type of recurrent neural network capable of learning order dependence in sequence prediction problems.\n",
    "\n",
    "### Read more about LSTM and Deep Neural Networks at:\n",
    "### https://blog.statsbot.co/time-series-prediction-using-recurrent-neural-networks-lstms-807fa6ca7f\n",
    "### https://towardsdatascience.com/a-gentle-introduction-to-neural-networks-series-part-1-2b90b87795bc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LSTM needs to define (Batch Size x Nbr of Batches x Features)\n",
    "###### 3-D tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_t = X_train[:,None]\n",
    "X_test_t = X_test[:,None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1c2e275cc0>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(7)\n",
    "\n",
    "K.clear_session()\n",
    "model = Sequential()\n",
    "\n",
    "model.add(LSTM(20, input_shape=(1,1)))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer=Adam(lr=0.001))\n",
    "model.fit(X_train_t, y_train, validation_data=(X_test_t, y_test), epochs=1000, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c2edee9e8>]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(model.history.history['val_loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c2f0b9f98>]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(model.history.history['val_loss'][5:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c2eeb04e0>]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_test_LSTM = np.insert(X_test,0,X_train[-1][0]).reshape(-1,1)\n",
    "X_test_LSTM = X_test_LSTM[:,None]\n",
    "X_test_LSTM.shape\n",
    "predictions = model.predict(X_test_LSTM)\n",
    "plt.figure(figsize=(20,6))\n",
    "plt.plot(y_test)\n",
    "plt.plot(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_inv_scaled = sc.inverse_transform(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.013581\n",
      "RMSE: 0.116540\n",
      "R-Squared: 0.448356\n"
     ]
    }
   ],
   "source": [
    "print('MSE: %f'%mean_squared_error(test,predictions_inv_scaled))\n",
    "print('RMSE: %f'%np.sqrt(mean_squared_error(test,predictions_inv_scaled)))\n",
    "print('R-Squared: %f'%r2_score(test,predictions_inv_scaled))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BIG PROBLEM WITH FORECASTING NEURAL NETWORKS WE BUILT ABOVE:\n",
    "#### Figure out what the problem is\n",
    "#### Start a discussion and try to solve the problem\n",
    "##### Hint: Time series forecast shoud never be based on ground truth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
